Geology and Hazards
The urban lifeline safety project is a major national livelihood project, which ensures the safe operation of urban gas, water, electricity and other systems. Nowadays, the project is confronted with three types of difficulties: Difficulties in mechanism discovery and experimental reproduction; difficulties in risk detection and early identification, and difficulties in accurate early warning and collaborative prevention and control. In order to reveal the mechanism and reproduce the experiments, a full-size urban lifeline multi-hazard large-scale scientific device is firstly developed in the world, a series of dis-aster modes with 5 categories and 169 kinds of disasters coupled are established, based on which a comprehensive risk dynamic assessment method coupled with underground pipelines and above-ground disaster-bearing body is presented. To improve the risk detection and early identification, highly sensitive gas sensing laser chip as well as detector with active grating and wavelength-biased are invented. We develop an intelligent ball for leakage located in water supply pipe network based on the inertial guidance, force balance, acoustic spectrum and BeiDou. Bridge overall modal (fingerprint) monitoring technology is break-through. In order to solve the problem of accurate early warning and collaborative prevention and control, an urban lifeline safety risk physical heuristic artificial intelligence early warning technology is presented, based on which a monitoring and early warning system is developed with collaborative sensing, fusion monitoring, targeting early warning, linkage response and other functions. The related studies result in the development of the safety and emergency response industry, followed by the pioneering and substantial contributions to the prevention and control of urban lifeline risks.
In mid- to late- April 2024, an extreme heavy rainfall event occurred in Shaoguan City, Guangdong Province, inducing a large number of landslides in Jiangwan Town, Shaoguan. People lost connection with the outside world for nearly 36 hours, which aroused widespread social concern. Rapidly and accurately identifying the basic characteristics of landslides, development and distribution patterns and formation conditions is crucial for disaster emergency decision-making and risk elimination and disposal.
Using the post-disaster optical remote sensing images and combining with deep learning model, the rainfall-induced landslides in Jiangwan Town, Shaoguan, were quickly and automatically identified.
After manually calibration, a total of 1 192 landslides were deciphered, with a total area of about 3.14 km². The scale of the landslides was dominated by small and medium-sized landslides, which were mainly distributed as an aggregated belt along the river in the northeast-southwest direction, with a significant characteristic of concentrated occurrence. Spatial statistical analysis showed that the landslides were mainly distributed on concave slopes with slopes of 10°-30° in the range of 200-300 m elevation. Further quantitative analysis of the geomorphic controlling factors of landslides using the random forest model and SHAP theory reveals that different topographic and geomorphic factors have different degrees of nonlinear effects on landslide formation, and that multiple factors such as elevation, slope, and catchment conditions are coupled to jointly control the formation of landslides.
This paper highlights the great advantage of deep learning-based intelligent identification and analysis technology in the emergency investigation and formation conditions analysis of landslide disasters, which can provide important technical support for the rapid assessment of disaster losses and risk identification.
The construction of effective path set is the key link of traffic control and guidance of highway network, and plays an important role in post disaster path planning. Usually when a disaster occurs, there is a problem that the current emergency route planning does not incorporate the dynamic changes of disasters in time, which affects the subsequent emergency evacuation and rescue and relief.
To solve this problem, a double-layer road network model is proposed, which couples the topological structure of the highway network with the traffic flow and the state of disasters and events. The linear reference and dynamic segmentation technology is introduced to associate the routes and events and the depth first search algorithm is improved based on Dijkstra algorithm.
(1) Double-layer road network model reflects the topological relationship between road sections, stores the dynamic attribute information of road sections and realizes the search and construction of effective path sets. (2) Improved DFS algorithm reduces the computational complexity and proposes an effective path search algorithm combining with time-varying road network, disasters and traffic conditions.
The example application and verification in the study area shows that the method can dynamically search the effective path set according to time-varying traffic conditions and disasters. This model enhances the expression and analysis ability of road data, and is able to serve traffic analysis and control under the change of disaster situation, and is suitable for the post disaster traffic operation situation assessment of road network.
Studying the spatiotemporal changes of groundwater storage(GWS) and its sustainable spatiotemporal evolution characteristics in the upper reaches of the Yellow River Basin can provide a valuable reference for the sustainable and rational development of water resources in the Yellow River Basin.
The changes of terrestrial water storage (TWS) and GWS in the upper reaches of the Yellow River Basin from April 2002 to December 2022 were estimated using Masson data and spherical harmonic (SH) data from GRACE (gravity recovery and climate experience) and GRACE-FO (GRACE follow on) gravity satellites, combined with prior hydrological models, and their temporal and spatial variation characteristics were also analyzed. The sustainability index (SI) of water resources in the study area was further calculated to evaluate the sustainability of regional groundwater. And the correlation and contribution of precipitation change, normalized difference vegetation index (NDVI), evapotranspiration (ET) and regional GWS were discussed.
The change of GWS in the study area showed an overall downward trend at a rate of approximately -3.89 ± 0.37 mm / a, and showed obvious spatial characteristics of the difference between the southern and northern values, which was consistent with the monitoring results of the measured wells (correlation was approximately 0.73). During the study period, the regional groundwater was almost in a state of severe unsustainability, and the spatial sustainability also gradually decreased from south to north, with an average sustainability index of only 0.38. The size of the contribution measure shows that NDVI has the greatest impact on the change of GWS in the study area, ET has the second highest impact,and the rainfall has the smallest; there was a significant negative correlation between NDVI, ET and regional GWS changes (correlation coefficients were approximately -0.76 and -0.77, respectively). The rainfall in the south and north of the study area was positively correlated with the corresponding GWS changes (correlation coefficients were approximately 0.54 and 0.50, respectively).
This study effectively evaluated the changes of TWS and GWS in the upper reaches of the Yellow River Basin over the past 20 years, and reasonably evaluated the spatiotemporal distribution characteristics of groundwater sustainability in the upper reaches of the Yellow River, as well as the correlation between external influences and GWS.
In order to improve the efficiency of landslide extraction and explore the spatial-temporal distribution characteristics of regional landslides and single landslide, a rapid landslide extraction model is designed to provide a scientific basis for landslide disaster prevention and management.
Based on multi-temporal domestic high-resolution remote sensing satellite images, advanced land observing satellite 12.5 m digital elevation model, and historical landslide hidden danger points, this study selected 8 landslide-prone townships located in the northwest of Fengjie County, Chongqing city,China to form the study area. Feature optimization based on SHAP in terpretation framework and Bayesian hyperparameter automatic optimization based on Optuna framework are introduced into XGBoost algorithm to construct and optimize the landslide extraction model. The study realized rapid extraction of landslide spatial information and quantitative analysis of landslide spatialtemporal distribution in 2013, 2015, 2018 and 2020.
In the comparison of models accuracy, precision, Kappa coefficient and area under curve value of landslide extraction model constructed by optimized XGBoost basic algorithm are 96.26%, 90.91%, 0.860 2 and 0.970 5, respectively, which higher than GBDT, LightGBM and Adaboost.
From 2013 to 2020, the overall development degree of landslides in the study area is relatively high, and the spatial distribution of landslides is uneven among villages and towns, and the landslides is distributed on both sides of the river valley and the river, showing the characteristics of regional concentration. From 2013 to 2020, Miaowan landslide has high development intensity, strong activity, repeated resurrection phenomenon and induced new landslides. The slope of the landslide is about 25°-45°, and it’s topographic characteristics change little, significant changes mainly focus on color, texture, geometry and vegetation coverage.
Pixel offset tracking (POT) for optical remote sensing imagery is widely used to invert coseismic deformation fields and monitor landslides. Traditional pixel offset tracking method estimates the displacement of the central pixel by searching for the matching window with the highest correlation, which is computationally inefficient and suffers from inaccurate deformation boundary extraction due to the decoherence effects in the region with dynamical deformation. We introduce the optical flow field model commonly used in computer vision to the pixel offset tracking problem to obtain accurate surface deformation efficiently.
The optical flow field method applicable to optical remote sensing images and the improved inversion algorithm for the time series analysis are proposed to inverse the surface deformation. Experiments on the simulated coseismic deformation fields in Tajikistan are detailed to assess the feasibility and the minimum detectable deformation of the optical flow field method. The advantages of the proposed method over computational cost and deformation boundary extraction accuracy are illustrated by the co-seismic deformation field of the California earthquake and the displacement of the Baige landslide. Furthermore, the performance on estimating large gradient deformation and the robustness of the improved time series inversion algorithm are discussed by analyzing the time series deformation of the Baige landslide.
The results show that compared with the traditional window correlation matching method, the optical flow field method has an offset tracking accuracy of 0.032 pixel, which improves the computational efficiency by about 20 times, and the accuracy of the deformation zone is improved by 25.9%. The time series weighted inversion algorithm reduces the uncertainties in the estimation of east-west and north-south displacements of optical remote sensing images by 16.2% and 12.4%, respectively.
The proposed method alleviates the pixel offset tracking problem in the boundary region with large gradient deformation.
Mine slope instability is one of the main factors restricting the safety production of open-pit mines in China. Ground-based synthetic aperture radar interferometry technology has been gradually introduced into the application of slope safety monitoring and early warning prediction in open-pit mines. However, the high-frequency rolling update characteristics of ground radar interferometry data lead to large data error accumulation and unobvious curve mutation characteristics.
Processing the original data by dislocation subtraction and velocity reciprocal method can effectively reduce the vibration of high-frequency data, improve the readability of critical sliding data. After data processing, it can highlight the trend characteristics of key deformation data. The research is based on the analysis of cumulative displacement curve, velocity curve and reciprocal velocity curve group treated with different periods.
It is found that there are three characteristic points in the curve group: Sudden deformation increase point, velocity increase point and stable vibration point. Through these characteristic points, the slope landslide disaster can be predicted. The trend of key deformation data can be highlighted by using the three feature points of deformation sudden increase point, velocity growth point and stable vibration point.
Through the identification of three feature points, the possible landslide can be effectively identified in advance and the landslide time can be predicted, which provides a new technical path and solution for landslide early warning and prediction analysis based on ground-based interferometric radar.
Land subsidence caused by long-term over-exploitation of groundwater is one of major problems in Beijing. Since the opening of the South-to-North Water Transfer Project, the problem of water shortage in Beijing has been greatly alleviated, and the pressure of land subsidence has been reduced to a certain extent.
In order to analyze the development of land subsidence after the start of the South-to-North Water Transfer in Beijing, ascending and descending time-series interferometric synthetic aperture radar (InSAR) technique is used to monitor land subsidence in Beijing. First, the mean deformation velocity and cumulative deformation in line of sight in Beijing from January 2015 to December 2020 is obtained by the small baseline subset InSAR. Second, the robust least square fitting method is used to fuse the deformation results of the lifting rail, after that the global positioning system monitoring data are compared with the fusion results of lifting rail. Finally, the variation trend between the deformation results obtained by the robust least quadratic fitting and groundwater data is analyzed.
The deformation results show that the center of Beijing is basically stable and the deformation distribution is not uniform. The maximum ascending annual deformation velocity and the maximum ascending cumulative deformation amount reach -134 mm/a and -697 mm respectively. The maximum descending annual deformation velocity and the maximum descending cumulative deformation amount reach -135 mm/a and -734 mm respectively. And the fusion results obtained by the least square fitting method has reliability and accuracy.
The subsidence rate in Beijing shows a decreasing trend with the gradual increase of groundwater level. In general, the middle route of South-to-North Water Transfer Project has alleviated the expansion trend of land subsidence in Beijing to a certain extent.
In the research work of reservoir landslide displacement prediction, due to the lag of reservoir water level response, it is difficult for the traditional landslide displacement prediction model to analyze the monotonically increasing step deformation characteristics, which seriously affects the prediction results, and it is necessary to establish a landslide displacement prediction model that can consider the time lag effect.
We analyze the time lag effect of reservoir levels separately through grey correlation, account for the cumulative effect of earlier rainfall, and consider the effect of earthquake on landslide deformation, and establishe an autoregressive distributed lag landslide displacement prediction model that can be applied to engineering sites.
The results show that: (1) The engineering case study concluded that rising reservoir levels and earthquakes were the main triggering factors for the increased deformation of the landslide, and the lag time of reservoir levels acting on landslide deformation was 8 days. (2) The correlation coefficient between the cumulative landslide displacement and the actual displacement calculated by the new model is as high as 0.992 7, with a root mean square error of 14.11 mm. (3) The calculation of trend speed ratio indicators can provide a new sensitivity evaluation parameter for landslide monitoring and early warning.
The study establishes a physically significant prediction model for reservoir bank landslide displacements, provides a comprehensive analysis of landslide displacements, achieves a quantitative calculation of the seismic contribution to landslide displacement evolution, and provides new technical support for the safety risk management of the whole process of reservoir bank landslide evolution during the water storage period.
As a continuation of mining subsidence, the surface secondary subsidence (including sinking and uplift) of closed mines poses a potential threat to the safety of surface buildings-structures in mining areas. However, at this stage, the law analysis of the surface secondary subsidence of closed mines under different geological mining conditions is not yet comprehensive. Due to the high underground water level, thick Quaternary loose layers, and multiple coal seams in the Huainan mining area, monitoring and analyzing the surface secondary subsidence of closed mines in the Huainan mining area has important theoretical and practical values.
First, in order to verify the reliability of the monitoring results, the StaMPS software is employed to simultaneously perform persistent scatterers interferometry and small baseline subset processing. Amplitude dispersion index and amplitude difference dispersion index are used to select coherent points, respectively. Then, the unwrapped phases of the coherent points are obtained by a three-dimensional phase unwrapping algorithm. Finally, the surface subsidence of the coherent points are obtained by using temporal-domain low-pass and spatial-domain high-pass filtering.
The results show that: (1) The surface uplifts after the closure of Xinjisan and Lizuizi-Xinzhuangzi-Xieyi mines, with a maximum uplift rate of 51.1 mm/a, and is located in Xinzhuangzi mine, while the surface of Panyi mine is still sinking due to its late closure in September 2018. (2) The surface secondary subsidence law of closed mines in Huainan mining area is sinking stage⁃stable stage⁃uplift stage, which is consistent with the surface subsidence law of closed mines in Xuzhou mining area, but the sinking, stable and uplift stages do not necessarily occur successively over time. (3) There may be a hydraulic connection between Lizuizi, Xinzhuangzi, and Xieyi mines. The groundwater first rises from the junction of Xinzhuangzi and Xieyi mines and then flows to the southeast and northwest sides.
Although the law of surface subsidence in the closed mines of Huainan and Xuzhou mines is relatively consistent, there are differences in the law of surface uplift due to different hydrogeological mining conditions. Therefore, in future work, we will continue to pay attention to the law of surface secondary subsidence of closed mines under different geological mining conditions.
The landslides disaster is one of the most important factors for the construction of major infrastructure in the western China. How to effectively and reliably carry out wide-area landslide susceptibility prediction has always been a frontier difficulty in domestic and foreign studies. However, the present data-driven methods for prediction of landslide susceptibility in large-scale and complex scenarios still face two major issues:(1) The difference between various landslide inducing environments in a wide range of scenarios, would cause the difficulty to applying a single model to account for multiple landslide phenomenon;(2) small sample problem: Complex environmental tasks require models with large capacity and strong representative power, but there is a lack of sufficient landslide samples in practice.
In response to the above problems, this paper takes Qijiang and Fuling District of Chongqing City, China as an example, proposes a local prediction strategy, and introduces the idea of meta-training an intermediate representation suited to be generalized, that can be adapted to the landslide sensitivity prediction task cor-responding to the current local area, with only a very small number of samples in the local area, and number of iterations. Thus, the two issues mentioned can be well settled.
The proposed method is different from traditional methods such as support vector machines, multilayer perceptrons, and random forests, which require a large number of samples and gradient iterations to train the supervised model. Instead, only a small sample is required to fine-tune the intermediate model, which still improves the global accuracy by 1%-5%, the precision by 1%-3%, the
The meta-learning paradigm enables few-shot adaptation of landslide susceptibility prediction model with superior statistical performance.
With the development of Shenzhen city, China, land renovation is more frequent. At the same time, affected by the subtropical monsoon climate, the area under the jurisdiction has abundant rainfall and dense vegetation coverage, making it difficult to identify the hidden dangers of geological hazards widely distributed on artificial slopes and natural slopes. Therefore, it is necessary to develop a set of hazard evaluation system of geological disaster that can solve the unique terrain and climate conditions in Shenzhen, so as to achieve the purpose of preventing disasters in advance and reducing casualties.
(1) On the basis of high-precision digital elevation model of Shenzhen city obtained by airborne light detection and ranging(LiDAR), about 3 500 slope disaster prone points in Shenzhen are obtained through data collection, remote sensing interpretation and field verification. The sample library expanded 330% after proofreading.(2) Taking 3 major factors (8 factors) of terrain, geological structure and human engineering activities into comprehensive consideration, and based on the rainfall-induced disaster mechanism, a rainfall collection factor is proposed, and the weight of evidence method is used to complete the geological disaster hazard evaluation model under rainfall-induced conditions.(3) The threshold determination method of“key point control”under the actual background of single disaster is proposed, and the classification of the risk assessment model is completed.
The area under curve value of receiver operating characteristic curve model reaches 0.903, indicating that the model has a good effect on disaster forecasting. LiDAR technology can improve the identification accuracy of geological hazards in cities under dense vegetation coverage.
Based on airborne LiDAR data, through a series of means such as expansion of disaster database, analysis of disaster distribution law, establishment of disaster evaluation factors, and classification of risk levels, it can form a refined evaluation system for the hazard evaluation of the slope in densely vegetated areas under the influence of the subtropical monsoon climate.
Because of high frequency of extreme weather, railway subgrade disaster shows a trend of increasing, great harmfulness and is hard to detect in advance. Even a small-size subgrade disaster may cause railway paralysis, which prevents the development of transportation. Many incidents illustrate that the earliest subgrade disaster always appeared in areas where no case ever reported before. It means that traditional monitoring methods are hard to detect hidden danger area. Thus, a promising method is urgent to be proposed for meeting the monitoring requirement for railway safety.
First, the characteristics of subgrade disaster are summarized under different disaster-pregnant environments, and the advantages of various monitoring methods are discussed to find a collaborative applications method for early identification of subgrade disaster. The potential hazards that may cause damage to subgrade structure and the deterioration degree of subgrade are considered as the main monitoring object of subgrade service status, including subgrade deformation monitoring, structural health monitoring, geological hazard monitoring along the railway, track irregularity, and external environmental monitoring. Second, an investigative approach based on the integration of space-air-train-ground muti-source techniques is proposed to detect the geohazards and monitor the service status of railway subgrade. It means that collaborative application of different monitoring methods, cooperative analysis of different scales and resolutions data and collaboration of between various railway departments are required. Two monitoring schemes for subgrade disasters and service status of subgrade are proposed based on the integration of multi-source and multi-scale monitoring technique. Finally, the development direction of subgrade service status monitoring is discussed.
This monitoring system has been applied in the identification of subgrade service status in the Shanghai-Nanjing high speed railway, which can quickly investigate the location of the disasters and the deterioration degree of subgrade.
On September 20, 2019, the Jungong ancient landslide in Lajia Town, Maqin County, Qinghai Province, China suffered a local failure, which led to the interruption of traffic and seriously threatened the safety of local residents' lives and properties. It is urgent to find out the deformation area and deformation law of the ancient landslide, and to provide the support for prevention and control enginee-ring design, monitoring and early warning.
First, using high-resolution satellite images, digital elevation model data and combined with field investigations, the ancient landslide were zoned based on landslide morphological characteristics and signs of deformations; further, Sentinel-1 radar satellite descending data from January 2017 to December 2020 were used to analyze the surface deformation characte-ristics and deformation patterns of the ancient landslide based on small baseline subset interferometric synthetic aperture radar technology (SBAS-InSAR).
Based on the morphological characteristics and deformation signs of the landslide, the ancient landslide was divided into four sub-areas. The SBAS-InSAR results show that the ancient landslide is in a continuous slow creeping state. The strong deformation area of the landslide is mainly located in the road excavation section. Human activities greatly disturb the stability of the ancient landslide. The deformation rate of the strong deformation area of the landslide has a good relationship with rainfall.
Although the ancient landslide has been partially treated with anti-slide piles and other projects, the ancient landslide has multi-level sliding surfaces, and the depth of the existing anti-slide piles is not enough. Although the ancient landslide has played a certain role in anti-slide, it has not completely prevented the creep deformation of the landslide. It is suggested that the subsequent treatment projects should use drilling and other exploration techniques to find out the depth of the multi-level sliding surfaces before designing, and installing on-site real-time monitoring and early warning equipment such as crack meters in the strongly deformed areas. Combined with the medium and long-term monitoring of radar satellite InSAR, a point-surface monitoring and early warning system could be realized.
The continuous exploitation of the Liaohe Delta oilfield has resulted in severe surface subsidence, impacting oil recovery rates, production operations, and posing threats to surface infrastructure and ecological environments. To ensure the safe exploitation of underground fluid resources and protect the regional environment, monitoring implementation is needed for this region.
The method of small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technology with coherence information as weights was used to analyze the surface deformation. Using the fusion decomposition of synthetic aperture radar descending and ascending orbit results to extract both vertical and horizontal east-west deformation within the Shuguang oilfield area. Subsequently, a reservoir compaction-induced subsidence inversion model is applied to the Shuguang oilfield to simulate and interpret the observed subsidence phenomena, linking them to the underground fluid resource exploitation activities.
The results reveal significant ground subsidence throughout the Liaohe Delta region, particularly in the Shuguang oilfield and Huanxiling oilfield. The average line of sight subsidence rates reaching 158 mm/a and 73 mm/a, respectively. In the Shuguang oilfield, there is horizontal movement towards the subsidence center, with approximately equal magnitudes of movement on the east and west sides. The maximum horizontal movement rate is observed to be -62 mm/a (westward motion). Furthermore, the reservoir compaction and subsidence model based on Shuguang oilfield reservoir parameters effectively invert the maximum subsidence position at the center of the oilfield, with subsidence range and magnitude consistent with the InSAR observation results.
The study concludes that continuous exploitation of oil has led to significant ground subsidence in the Liaohe Delta region, especially in Shuguang oilfield and Huanxiling oilfield, with clear patterns of subsidence and horizontal movement detected using SBAS-InSAR technology. The developed reservoir compaction-induced subsidence inversion model proves effective in simulating subsidence phenomena associated with oilfield operations. These findings underscore the importance of monitoring and managing subsidence risks to ensure the safe exploitation of underground resources and to protect regional ecological environments in the Liaohe Delta region.
Geological hazard points and hidden danger points are the data basis for geological hazard evaluation, while the existing records of geological hazard points have poor timeliness and are incomplete. To solve this problem, the deformation information obtained by multi-temporal interferometric synthetic aperture radar (InSAR) was integrated into the geological hazard evaluation model. And we explore how to make better use of the deformation information.
The greater the deformation level, the greater the possibility of geological hazards. This paper not only takes the deformation points obtained by multi-temporal InSAR as the geological hazard points/hidden danger points, but also integrates the deformation level information obtained by multi-temporal InSAR as an evaluation factor into the susceptibility evaluation model, making full use of the effective deformation information obtained by multi-temporal InSAR. And the evaluation model adopts the coupling model based on information value model and the analytic hierarchy process model to obtain the susceptibility evaluation and zoning of the geological hazards in Baiyin City, Gansu Province,China.
Through the verification of the existing geological disaster point data, it is found that the partitions in this paper are in good agreement with the existing geological hazard points distribution.In the designated extremely high-prone areas, there are nearly 8 geological disaster points within 10 km2, while less than one in the extremely low-prone areas.
The multi-temporal InSAR deformation information added to the geological hazard evaluation model greatly improves the timeliness and quantity of records of geological hazard points/hidden points. However, only one kind of synthetic aperture radar data cannot completely identify all geological hazard points/hidden danger points, due to the limitations of incidence angle and microwave wavelength. In the futher work, we will focus on the combination of multiple deformation monitoring technologies to jointly monitor surface deformation, such as multi-sensor and multi-track InSAR technology, airborne light laser detection and ranging and high-resolution optical remote sensing.
Flash floods are natural disasters caused by sudden rise in water levels in mountainous rivers, which are characterized by instantaneity and great destructiveness. In recent years, the frequent occurrence of flash floods in Longnan city, Gansu province, has posed a serious threat to the safety of local people's lives and property, thus it is urgent to carry out a risk assessment of flash floods in this region.
This study takes Longnan city as the study area, and utilizes the MaxEnt model combining with the particle swarm algorithm to evaluate the vulnerability of study area based on 834 flash flood hazard points investigated and 32 disaster-causing factors. It also predicts the spatial pattern changes and potential mass migration trends of the future flash flood vulnerability areas based on three periods of climate data from the current period (2021—2040) and the future period (2041—2060, 2061—2080, 2081—2100).
The area under receiver operating characteristic curve of the results of the study in each period is above 0.85, which indicates that the precision of the results of the method is good. The main cause factors in this study area are driest month precipitation, monthly mean diurnal temperature difference, coefficient of variation of precipitation, warmest month maximum temperature, land use, distance from the river, soil texture, profile curvature, elevation, and topographic relief. The flash flood-prone areas in the study area varies in different periods, but are mainly distributed in Wudu District, Wen County and Tanchang County, and the simulation results for the three future periods (2041—2060, 2061—2080, 2081—2100) reflected a decreasing trend compared with the current period (2021—2040).
Flood disaster is a very destructive natural disaster. The main reasons for its generation include heavy rainfall, storm surge and dam break. When flood disaster occurs in populated areas such as cities and towns, the flood will directly threaten the safety of life and property of residents. Also it will cause paralysis of land and underground transportation, interruption of water and electricity transportation. In the process of flood rescue, quick and accurate identification of flooded roads is conducive to planning appropriate personnel transfer and material transportation routes, and reducing subsequent losses caused by floods. Aiming at the problem that roads in the flood disaster scenario cannot be automatically and correctly identified, this paper proposes an end-to-end flooded road detection method based on deep learning.
The proposed method uses a three-branch encoder-decoder structure and uses strip convolution, in which the efficient extraction of linear features is realized. And the coordination dual attention mechanism can effectively guide the network, and realize the identification of road areas. The method can effectively utilize the historical optical remote sensing images before the disaster and the real-time optical remote sensing during the disaster. The image is applied to detect the flooded and non-flooded roads in the disaster-stricken area, and a comparative experiment is carried out on the self-built dataset.
The experimental results show that the precison and recall rate are 0.838 1 and 0.666 8 on pre-disaster road, 0.796 6 and 0.607 4 on post-disaster road, 0.780 0 and 0.661 4 on affected road respectively.
The proposed method has achieved the goal of automatically identifying the flooded roads in the flood-stricken area. The ability of detecting flooded roads and non-flooded roads can provide strong support for flood disaster rescue and reduce losses of life and property caused by flood disasters.
Convolutional neural network (CNN) was widely used in landslide susceptibility assessment because of its powerful feature extraction capability. However, with the demand for scene diversification and high accuracy, the algorithm of CNN was constantly improved. The practice of improving accuracy by deepening the network hierarchy or combining other models tended to increase the number of model parameters and the amount of calculation greatly, which led to the difficulty of model training or the overfitting of results, thus limiting its practical application. We want to solve the above problems from the model structure.
We proposed to build a multi-dimensional CNN coupling model. Through the asymmetric aggregation of feature maps, one-dimensional CNN(1D-CNN) and two-dimensional CNN(2D-CNN) were connected to maintain network depth, limit model parameters, and reduce computation. The parameters sharing of multi-dimensional convolution kernels was used to capture the deep coupling features of different dimensions and different landslide factors, so as to make full use of features and avoid overfitting. Taking Sedongpu gully in Xizang,China as the experimental area, this paper selected 11 kinds of landslide influencing factors to analyze the landslide susceptibility.
The results show that the multi-dimensional CNN coupling structure had the same training efficiency as the shallow 2D-CNN with fewer parameters due to the reduced computational effort. While compared with the deep 2D-CNN with the approximate number of parameters, the training time of the proposed method was significantly reduced and the training cost was lower. In addition, the coupled model had a stronger feature learning ability than the independent 1D-CNN and 2D-CNN, and therefore obtained higher model accuracy. Under each confusion matrix metric of the testing data, the coupled model received higher scores, and thus obtained more reliable landslide susceptibility assessment results.
The multi-dimensional CNN coupling model proposed in this paper is a reliable method applicable to landslide susceptibility assessment. This study provides new theoretical guidance and technical support for further landslide hazard monitoring and prevention.
The response law of ancient (old) landslides in the reservoir area is an important research topic. Previous research primarily analyzed real-time surface displacement and reservoir water level data. However, professional monitoring conditions are often lacking on most reservoir bank slopes. This complicates tracking the landslide's historical deformation. Satellite and airborne remote sensing platforms enable multi-scale, long-term monitoring of landslide deformation and damage.
This study employs multi-source three-dimensional observation technologies including aerial, space-based, and terrestrial platforms to monitor the deformation and evolution of the Pubugou Hydropower Station's Hongyanzi landslide over approximately 10 years. It utilizes unmanned aerial vehicle photography (optical imaging) and light detection and ranging (LiDAR) for detailed topographic mapping and deformation analysis from 2009 to 2020. Additionally, time-series interferometric synthetic aperture radar (InSAR) technology is used to track long-term surface deformations from October 2014 to July 2020. Field investigations have identified typical deformation and failure characteristics of the landslide, incorporating geological conditions and external factors such as rainfall and reservoir water levels to analyze causal mechanisms and dynamic trends.
The irregularly semicircular Hongyanzi landslide spans 20-50 m in thickness, encompasses approximately 15.53 million m³ in volume, and slides at an approximate bearing of 340°. Composed of quaternary pebbled stones, silty sand, and clay, the landslide's bed slopes between 20° and 25°. Its lithology includes Emeishan Formation basalt and Yangxin Formation dolomite. Existing since 2006 or earlier, the landslide features elements like walls and steps. LiDAR imagery from 2009 clearly delineates its boundaries, though it shows no new signs of deformation or failure. Following reservoir impoundment, the reactivated landslide develops new, widening cracks along its rear edge. Post-reactivation, the landslide predominantly undergos uniform deformation, with more significant movement at the trailing edge than the leading edge, without marked acceleration. Heavy rainfall is the most significant control factor, imparting stepwise deformation characteristics to the landslide.
A comprehensive analysis of multi-source data reveals that phenomena like the Hongyanzi landslide exhibit typical long-term, gradual, and seasonal movements. Long-term InSAR effectively captures these characteristics. Multi-stage optical remote sensing and surface point cloud data from LiDAR, after vegetation removal, enable more intuitive comparisons of macroscopic deformation across different landslide areas. Integrating this with geological assessments and field investigations allows for detailed engineering analyses to ascertain the causes, patterns, and future trends of landslide activity.
The urban lifeline safety project is a major national livelihood project, which ensures the safe operation of urban gas, water, electricity and other systems. Nowadays, the project is confronted with three types of difficulties: Difficulties in mechanism discovery and experimental reproduction; difficulties in risk detection and early identification, and difficulties in accurate early warning and collaborative prevention and control. In order to reveal the mechanism and reproduce the experiments, a full-size urban lifeline multi-hazard large-scale scientific device is firstly developed in the world, a series of dis-aster modes with 5 categories and 169 kinds of disasters coupled are established, based on which a comprehensive risk dynamic assessment method coupled with underground pipelines and above-ground disaster-bearing body is presented. To improve the risk detection and early identification, highly sensitive gas sensing laser chip as well as detector with active grating and wavelength-biased are invented. We develop an intelligent ball for leakage located in water supply pipe network based on the inertial guidance, force balance, acoustic spectrum and BeiDou. Bridge overall modal (fingerprint) monitoring technology is break-through. In order to solve the problem of accurate early warning and collaborative prevention and control, an urban lifeline safety risk physical heuristic artificial intelligence early warning technology is presented, based on which a monitoring and early warning system is developed with collaborative sensing, fusion monitoring, targeting early warning, linkage response and other functions. The related studies result in the development of the safety and emergency response industry, followed by the pioneering and substantial contributions to the prevention and control of urban lifeline risks.
In mid- to late- April 2024, an extreme heavy rainfall event occurred in Shaoguan City, Guangdong Province, inducing a large number of landslides in Jiangwan Town, Shaoguan. People lost connection with the outside world for nearly 36 hours, which aroused widespread social concern. Rapidly and accurately identifying the basic characteristics of landslides, development and distribution patterns and formation conditions is crucial for disaster emergency decision-making and risk elimination and disposal.
Using the post-disaster optical remote sensing images and combining with deep learning model, the rainfall-induced landslides in Jiangwan Town, Shaoguan, were quickly and automatically identified.
After manually calibration, a total of 1 192 landslides were deciphered, with a total area of about 3.14 km². The scale of the landslides was dominated by small and medium-sized landslides, which were mainly distributed as an aggregated belt along the river in the northeast-southwest direction, with a significant characteristic of concentrated occurrence. Spatial statistical analysis showed that the landslides were mainly distributed on concave slopes with slopes of 10°-30° in the range of 200-300 m elevation. Further quantitative analysis of the geomorphic controlling factors of landslides using the random forest model and SHAP theory reveals that different topographic and geomorphic factors have different degrees of nonlinear effects on landslide formation, and that multiple factors such as elevation, slope, and catchment conditions are coupled to jointly control the formation of landslides.
This paper highlights the great advantage of deep learning-based intelligent identification and analysis technology in the emergency investigation and formation conditions analysis of landslide disasters, which can provide important technical support for the rapid assessment of disaster losses and risk identification.
The construction of effective path set is the key link of traffic control and guidance of highway network, and plays an important role in post disaster path planning. Usually when a disaster occurs, there is a problem that the current emergency route planning does not incorporate the dynamic changes of disasters in time, which affects the subsequent emergency evacuation and rescue and relief.
To solve this problem, a double-layer road network model is proposed, which couples the topological structure of the highway network with the traffic flow and the state of disasters and events. The linear reference and dynamic segmentation technology is introduced to associate the routes and events and the depth first search algorithm is improved based on Dijkstra algorithm.
(1) Double-layer road network model reflects the topological relationship between road sections, stores the dynamic attribute information of road sections and realizes the search and construction of effective path sets. (2) Improved DFS algorithm reduces the computational complexity and proposes an effective path search algorithm combining with time-varying road network, disasters and traffic conditions.
The example application and verification in the study area shows that the method can dynamically search the effective path set according to time-varying traffic conditions and disasters. This model enhances the expression and analysis ability of road data, and is able to serve traffic analysis and control under the change of disaster situation, and is suitable for the post disaster traffic operation situation assessment of road network.
Studying the spatiotemporal changes of groundwater storage(GWS) and its sustainable spatiotemporal evolution characteristics in the upper reaches of the Yellow River Basin can provide a valuable reference for the sustainable and rational development of water resources in the Yellow River Basin.
The changes of terrestrial water storage (TWS) and GWS in the upper reaches of the Yellow River Basin from April 2002 to December 2022 were estimated using Masson data and spherical harmonic (SH) data from GRACE (gravity recovery and climate experience) and GRACE-FO (GRACE follow on) gravity satellites, combined with prior hydrological models, and their temporal and spatial variation characteristics were also analyzed. The sustainability index (SI) of water resources in the study area was further calculated to evaluate the sustainability of regional groundwater. And the correlation and contribution of precipitation change, normalized difference vegetation index (NDVI), evapotranspiration (ET) and regional GWS were discussed.
The change of GWS in the study area showed an overall downward trend at a rate of approximately -3.89 ± 0.37 mm / a, and showed obvious spatial characteristics of the difference between the southern and northern values, which was consistent with the monitoring results of the measured wells (correlation was approximately 0.73). During the study period, the regional groundwater was almost in a state of severe unsustainability, and the spatial sustainability also gradually decreased from south to north, with an average sustainability index of only 0.38. The size of the contribution measure shows that NDVI has the greatest impact on the change of GWS in the study area, ET has the second highest impact,and the rainfall has the smallest; there was a significant negative correlation between NDVI, ET and regional GWS changes (correlation coefficients were approximately -0.76 and -0.77, respectively). The rainfall in the south and north of the study area was positively correlated with the corresponding GWS changes (correlation coefficients were approximately 0.54 and 0.50, respectively).
This study effectively evaluated the changes of TWS and GWS in the upper reaches of the Yellow River Basin over the past 20 years, and reasonably evaluated the spatiotemporal distribution characteristics of groundwater sustainability in the upper reaches of the Yellow River, as well as the correlation between external influences and GWS.
In order to improve the efficiency of landslide extraction and explore the spatial-temporal distribution characteristics of regional landslides and single landslide, a rapid landslide extraction model is designed to provide a scientific basis for landslide disaster prevention and management.
Based on multi-temporal domestic high-resolution remote sensing satellite images, advanced land observing satellite 12.5 m digital elevation model, and historical landslide hidden danger points, this study selected 8 landslide-prone townships located in the northwest of Fengjie County, Chongqing city,China to form the study area. Feature optimization based on SHAP in terpretation framework and Bayesian hyperparameter automatic optimization based on Optuna framework are introduced into XGBoost algorithm to construct and optimize the landslide extraction model. The study realized rapid extraction of landslide spatial information and quantitative analysis of landslide spatialtemporal distribution in 2013, 2015, 2018 and 2020.
In the comparison of models accuracy, precision, Kappa coefficient and area under curve value of landslide extraction model constructed by optimized XGBoost basic algorithm are 96.26%, 90.91%, 0.860 2 and 0.970 5, respectively, which higher than GBDT, LightGBM and Adaboost.
From 2013 to 2020, the overall development degree of landslides in the study area is relatively high, and the spatial distribution of landslides is uneven among villages and towns, and the landslides is distributed on both sides of the river valley and the river, showing the characteristics of regional concentration. From 2013 to 2020, Miaowan landslide has high development intensity, strong activity, repeated resurrection phenomenon and induced new landslides. The slope of the landslide is about 25°-45°, and it’s topographic characteristics change little, significant changes mainly focus on color, texture, geometry and vegetation coverage.
Pixel offset tracking (POT) for optical remote sensing imagery is widely used to invert coseismic deformation fields and monitor landslides. Traditional pixel offset tracking method estimates the displacement of the central pixel by searching for the matching window with the highest correlation, which is computationally inefficient and suffers from inaccurate deformation boundary extraction due to the decoherence effects in the region with dynamical deformation. We introduce the optical flow field model commonly used in computer vision to the pixel offset tracking problem to obtain accurate surface deformation efficiently.
The optical flow field method applicable to optical remote sensing images and the improved inversion algorithm for the time series analysis are proposed to inverse the surface deformation. Experiments on the simulated coseismic deformation fields in Tajikistan are detailed to assess the feasibility and the minimum detectable deformation of the optical flow field method. The advantages of the proposed method over computational cost and deformation boundary extraction accuracy are illustrated by the co-seismic deformation field of the California earthquake and the displacement of the Baige landslide. Furthermore, the performance on estimating large gradient deformation and the robustness of the improved time series inversion algorithm are discussed by analyzing the time series deformation of the Baige landslide.
The results show that compared with the traditional window correlation matching method, the optical flow field method has an offset tracking accuracy of 0.032 pixel, which improves the computational efficiency by about 20 times, and the accuracy of the deformation zone is improved by 25.9%. The time series weighted inversion algorithm reduces the uncertainties in the estimation of east-west and north-south displacements of optical remote sensing images by 16.2% and 12.4%, respectively.
The proposed method alleviates the pixel offset tracking problem in the boundary region with large gradient deformation.
Mine slope instability is one of the main factors restricting the safety production of open-pit mines in China. Ground-based synthetic aperture radar interferometry technology has been gradually introduced into the application of slope safety monitoring and early warning prediction in open-pit mines. However, the high-frequency rolling update characteristics of ground radar interferometry data lead to large data error accumulation and unobvious curve mutation characteristics.
Processing the original data by dislocation subtraction and velocity reciprocal method can effectively reduce the vibration of high-frequency data, improve the readability of critical sliding data. After data processing, it can highlight the trend characteristics of key deformation data. The research is based on the analysis of cumulative displacement curve, velocity curve and reciprocal velocity curve group treated with different periods.
It is found that there are three characteristic points in the curve group: Sudden deformation increase point, velocity increase point and stable vibration point. Through these characteristic points, the slope landslide disaster can be predicted. The trend of key deformation data can be highlighted by using the three feature points of deformation sudden increase point, velocity growth point and stable vibration point.
Through the identification of three feature points, the possible landslide can be effectively identified in advance and the landslide time can be predicted, which provides a new technical path and solution for landslide early warning and prediction analysis based on ground-based interferometric radar.
Land subsidence caused by long-term over-exploitation of groundwater is one of major problems in Beijing. Since the opening of the South-to-North Water Transfer Project, the problem of water shortage in Beijing has been greatly alleviated, and the pressure of land subsidence has been reduced to a certain extent.
In order to analyze the development of land subsidence after the start of the South-to-North Water Transfer in Beijing, ascending and descending time-series interferometric synthetic aperture radar (InSAR) technique is used to monitor land subsidence in Beijing. First, the mean deformation velocity and cumulative deformation in line of sight in Beijing from January 2015 to December 2020 is obtained by the small baseline subset InSAR. Second, the robust least square fitting method is used to fuse the deformation results of the lifting rail, after that the global positioning system monitoring data are compared with the fusion results of lifting rail. Finally, the variation trend between the deformation results obtained by the robust least quadratic fitting and groundwater data is analyzed.
The deformation results show that the center of Beijing is basically stable and the deformation distribution is not uniform. The maximum ascending annual deformation velocity and the maximum ascending cumulative deformation amount reach -134 mm/a and -697 mm respectively. The maximum descending annual deformation velocity and the maximum descending cumulative deformation amount reach -135 mm/a and -734 mm respectively. And the fusion results obtained by the least square fitting method has reliability and accuracy.
The subsidence rate in Beijing shows a decreasing trend with the gradual increase of groundwater level. In general, the middle route of South-to-North Water Transfer Project has alleviated the expansion trend of land subsidence in Beijing to a certain extent.
In the research work of reservoir landslide displacement prediction, due to the lag of reservoir water level response, it is difficult for the traditional landslide displacement prediction model to analyze the monotonically increasing step deformation characteristics, which seriously affects the prediction results, and it is necessary to establish a landslide displacement prediction model that can consider the time lag effect.
We analyze the time lag effect of reservoir levels separately through grey correlation, account for the cumulative effect of earlier rainfall, and consider the effect of earthquake on landslide deformation, and establishe an autoregressive distributed lag landslide displacement prediction model that can be applied to engineering sites.
The results show that: (1) The engineering case study concluded that rising reservoir levels and earthquakes were the main triggering factors for the increased deformation of the landslide, and the lag time of reservoir levels acting on landslide deformation was 8 days. (2) The correlation coefficient between the cumulative landslide displacement and the actual displacement calculated by the new model is as high as 0.992 7, with a root mean square error of 14.11 mm. (3) The calculation of trend speed ratio indicators can provide a new sensitivity evaluation parameter for landslide monitoring and early warning.
The study establishes a physically significant prediction model for reservoir bank landslide displacements, provides a comprehensive analysis of landslide displacements, achieves a quantitative calculation of the seismic contribution to landslide displacement evolution, and provides new technical support for the safety risk management of the whole process of reservoir bank landslide evolution during the water storage period.
As a continuation of mining subsidence, the surface secondary subsidence (including sinking and uplift) of closed mines poses a potential threat to the safety of surface buildings-structures in mining areas. However, at this stage, the law analysis of the surface secondary subsidence of closed mines under different geological mining conditions is not yet comprehensive. Due to the high underground water level, thick Quaternary loose layers, and multiple coal seams in the Huainan mining area, monitoring and analyzing the surface secondary subsidence of closed mines in the Huainan mining area has important theoretical and practical values.
First, in order to verify the reliability of the monitoring results, the StaMPS software is employed to simultaneously perform persistent scatterers interferometry and small baseline subset processing. Amplitude dispersion index and amplitude difference dispersion index are used to select coherent points, respectively. Then, the unwrapped phases of the coherent points are obtained by a three-dimensional phase unwrapping algorithm. Finally, the surface subsidence of the coherent points are obtained by using temporal-domain low-pass and spatial-domain high-pass filtering.
The results show that: (1) The surface uplifts after the closure of Xinjisan and Lizuizi-Xinzhuangzi-Xieyi mines, with a maximum uplift rate of 51.1 mm/a, and is located in Xinzhuangzi mine, while the surface of Panyi mine is still sinking due to its late closure in September 2018. (2) The surface secondary subsidence law of closed mines in Huainan mining area is sinking stage⁃stable stage⁃uplift stage, which is consistent with the surface subsidence law of closed mines in Xuzhou mining area, but the sinking, stable and uplift stages do not necessarily occur successively over time. (3) There may be a hydraulic connection between Lizuizi, Xinzhuangzi, and Xieyi mines. The groundwater first rises from the junction of Xinzhuangzi and Xieyi mines and then flows to the southeast and northwest sides.
Although the law of surface subsidence in the closed mines of Huainan and Xuzhou mines is relatively consistent, there are differences in the law of surface uplift due to different hydrogeological mining conditions. Therefore, in future work, we will continue to pay attention to the law of surface secondary subsidence of closed mines under different geological mining conditions.
The landslides disaster is one of the most important factors for the construction of major infrastructure in the western China. How to effectively and reliably carry out wide-area landslide susceptibility prediction has always been a frontier difficulty in domestic and foreign studies. However, the present data-driven methods for prediction of landslide susceptibility in large-scale and complex scenarios still face two major issues:(1) The difference between various landslide inducing environments in a wide range of scenarios, would cause the difficulty to applying a single model to account for multiple landslide phenomenon;(2) small sample problem: Complex environmental tasks require models with large capacity and strong representative power, but there is a lack of sufficient landslide samples in practice.
In response to the above problems, this paper takes Qijiang and Fuling District of Chongqing City, China as an example, proposes a local prediction strategy, and introduces the idea of meta-training an intermediate representation suited to be generalized, that can be adapted to the landslide sensitivity prediction task cor-responding to the current local area, with only a very small number of samples in the local area, and number of iterations. Thus, the two issues mentioned can be well settled.
The proposed method is different from traditional methods such as support vector machines, multilayer perceptrons, and random forests, which require a large number of samples and gradient iterations to train the supervised model. Instead, only a small sample is required to fine-tune the intermediate model, which still improves the global accuracy by 1%-5%, the precision by 1%-3%, the
The meta-learning paradigm enables few-shot adaptation of landslide susceptibility prediction model with superior statistical performance.
With the development of Shenzhen city, China, land renovation is more frequent. At the same time, affected by the subtropical monsoon climate, the area under the jurisdiction has abundant rainfall and dense vegetation coverage, making it difficult to identify the hidden dangers of geological hazards widely distributed on artificial slopes and natural slopes. Therefore, it is necessary to develop a set of hazard evaluation system of geological disaster that can solve the unique terrain and climate conditions in Shenzhen, so as to achieve the purpose of preventing disasters in advance and reducing casualties.
(1) On the basis of high-precision digital elevation model of Shenzhen city obtained by airborne light detection and ranging(LiDAR), about 3 500 slope disaster prone points in Shenzhen are obtained through data collection, remote sensing interpretation and field verification. The sample library expanded 330% after proofreading.(2) Taking 3 major factors (8 factors) of terrain, geological structure and human engineering activities into comprehensive consideration, and based on the rainfall-induced disaster mechanism, a rainfall collection factor is proposed, and the weight of evidence method is used to complete the geological disaster hazard evaluation model under rainfall-induced conditions.(3) The threshold determination method of“key point control”under the actual background of single disaster is proposed, and the classification of the risk assessment model is completed.
The area under curve value of receiver operating characteristic curve model reaches 0.903, indicating that the model has a good effect on disaster forecasting. LiDAR technology can improve the identification accuracy of geological hazards in cities under dense vegetation coverage.
Based on airborne LiDAR data, through a series of means such as expansion of disaster database, analysis of disaster distribution law, establishment of disaster evaluation factors, and classification of risk levels, it can form a refined evaluation system for the hazard evaluation of the slope in densely vegetated areas under the influence of the subtropical monsoon climate.
Because of high frequency of extreme weather, railway subgrade disaster shows a trend of increasing, great harmfulness and is hard to detect in advance. Even a small-size subgrade disaster may cause railway paralysis, which prevents the development of transportation. Many incidents illustrate that the earliest subgrade disaster always appeared in areas where no case ever reported before. It means that traditional monitoring methods are hard to detect hidden danger area. Thus, a promising method is urgent to be proposed for meeting the monitoring requirement for railway safety.
First, the characteristics of subgrade disaster are summarized under different disaster-pregnant environments, and the advantages of various monitoring methods are discussed to find a collaborative applications method for early identification of subgrade disaster. The potential hazards that may cause damage to subgrade structure and the deterioration degree of subgrade are considered as the main monitoring object of subgrade service status, including subgrade deformation monitoring, structural health monitoring, geological hazard monitoring along the railway, track irregularity, and external environmental monitoring. Second, an investigative approach based on the integration of space-air-train-ground muti-source techniques is proposed to detect the geohazards and monitor the service status of railway subgrade. It means that collaborative application of different monitoring methods, cooperative analysis of different scales and resolutions data and collaboration of between various railway departments are required. Two monitoring schemes for subgrade disasters and service status of subgrade are proposed based on the integration of multi-source and multi-scale monitoring technique. Finally, the development direction of subgrade service status monitoring is discussed.
This monitoring system has been applied in the identification of subgrade service status in the Shanghai-Nanjing high speed railway, which can quickly investigate the location of the disasters and the deterioration degree of subgrade.
On September 20, 2019, the Jungong ancient landslide in Lajia Town, Maqin County, Qinghai Province, China suffered a local failure, which led to the interruption of traffic and seriously threatened the safety of local residents' lives and properties. It is urgent to find out the deformation area and deformation law of the ancient landslide, and to provide the support for prevention and control enginee-ring design, monitoring and early warning.
First, using high-resolution satellite images, digital elevation model data and combined with field investigations, the ancient landslide were zoned based on landslide morphological characteristics and signs of deformations; further, Sentinel-1 radar satellite descending data from January 2017 to December 2020 were used to analyze the surface deformation characte-ristics and deformation patterns of the ancient landslide based on small baseline subset interferometric synthetic aperture radar technology (SBAS-InSAR).
Based on the morphological characteristics and deformation signs of the landslide, the ancient landslide was divided into four sub-areas. The SBAS-InSAR results show that the ancient landslide is in a continuous slow creeping state. The strong deformation area of the landslide is mainly located in the road excavation section. Human activities greatly disturb the stability of the ancient landslide. The deformation rate of the strong deformation area of the landslide has a good relationship with rainfall.
Although the ancient landslide has been partially treated with anti-slide piles and other projects, the ancient landslide has multi-level sliding surfaces, and the depth of the existing anti-slide piles is not enough. Although the ancient landslide has played a certain role in anti-slide, it has not completely prevented the creep deformation of the landslide. It is suggested that the subsequent treatment projects should use drilling and other exploration techniques to find out the depth of the multi-level sliding surfaces before designing, and installing on-site real-time monitoring and early warning equipment such as crack meters in the strongly deformed areas. Combined with the medium and long-term monitoring of radar satellite InSAR, a point-surface monitoring and early warning system could be realized.
The continuous exploitation of the Liaohe Delta oilfield has resulted in severe surface subsidence, impacting oil recovery rates, production operations, and posing threats to surface infrastructure and ecological environments. To ensure the safe exploitation of underground fluid resources and protect the regional environment, monitoring implementation is needed for this region.
The method of small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technology with coherence information as weights was used to analyze the surface deformation. Using the fusion decomposition of synthetic aperture radar descending and ascending orbit results to extract both vertical and horizontal east-west deformation within the Shuguang oilfield area. Subsequently, a reservoir compaction-induced subsidence inversion model is applied to the Shuguang oilfield to simulate and interpret the observed subsidence phenomena, linking them to the underground fluid resource exploitation activities.
The results reveal significant ground subsidence throughout the Liaohe Delta region, particularly in the Shuguang oilfield and Huanxiling oilfield. The average line of sight subsidence rates reaching 158 mm/a and 73 mm/a, respectively. In the Shuguang oilfield, there is horizontal movement towards the subsidence center, with approximately equal magnitudes of movement on the east and west sides. The maximum horizontal movement rate is observed to be -62 mm/a (westward motion). Furthermore, the reservoir compaction and subsidence model based on Shuguang oilfield reservoir parameters effectively invert the maximum subsidence position at the center of the oilfield, with subsidence range and magnitude consistent with the InSAR observation results.
The study concludes that continuous exploitation of oil has led to significant ground subsidence in the Liaohe Delta region, especially in Shuguang oilfield and Huanxiling oilfield, with clear patterns of subsidence and horizontal movement detected using SBAS-InSAR technology. The developed reservoir compaction-induced subsidence inversion model proves effective in simulating subsidence phenomena associated with oilfield operations. These findings underscore the importance of monitoring and managing subsidence risks to ensure the safe exploitation of underground resources and to protect regional ecological environments in the Liaohe Delta region.
Geological hazard points and hidden danger points are the data basis for geological hazard evaluation, while the existing records of geological hazard points have poor timeliness and are incomplete. To solve this problem, the deformation information obtained by multi-temporal interferometric synthetic aperture radar (InSAR) was integrated into the geological hazard evaluation model. And we explore how to make better use of the deformation information.
The greater the deformation level, the greater the possibility of geological hazards. This paper not only takes the deformation points obtained by multi-temporal InSAR as the geological hazard points/hidden danger points, but also integrates the deformation level information obtained by multi-temporal InSAR as an evaluation factor into the susceptibility evaluation model, making full use of the effective deformation information obtained by multi-temporal InSAR. And the evaluation model adopts the coupling model based on information value model and the analytic hierarchy process model to obtain the susceptibility evaluation and zoning of the geological hazards in Baiyin City, Gansu Province,China.
Through the verification of the existing geological disaster point data, it is found that the partitions in this paper are in good agreement with the existing geological hazard points distribution.In the designated extremely high-prone areas, there are nearly 8 geological disaster points within 10 km2, while less than one in the extremely low-prone areas.
The multi-temporal InSAR deformation information added to the geological hazard evaluation model greatly improves the timeliness and quantity of records of geological hazard points/hidden points. However, only one kind of synthetic aperture radar data cannot completely identify all geological hazard points/hidden danger points, due to the limitations of incidence angle and microwave wavelength. In the futher work, we will focus on the combination of multiple deformation monitoring technologies to jointly monitor surface deformation, such as multi-sensor and multi-track InSAR technology, airborne light laser detection and ranging and high-resolution optical remote sensing.
Flash floods are natural disasters caused by sudden rise in water levels in mountainous rivers, which are characterized by instantaneity and great destructiveness. In recent years, the frequent occurrence of flash floods in Longnan city, Gansu province, has posed a serious threat to the safety of local people's lives and property, thus it is urgent to carry out a risk assessment of flash floods in this region.
This study takes Longnan city as the study area, and utilizes the MaxEnt model combining with the particle swarm algorithm to evaluate the vulnerability of study area based on 834 flash flood hazard points investigated and 32 disaster-causing factors. It also predicts the spatial pattern changes and potential mass migration trends of the future flash flood vulnerability areas based on three periods of climate data from the current period (2021—2040) and the future period (2041—2060, 2061—2080, 2081—2100).
The area under receiver operating characteristic curve of the results of the study in each period is above 0.85, which indicates that the precision of the results of the method is good. The main cause factors in this study area are driest month precipitation, monthly mean diurnal temperature difference, coefficient of variation of precipitation, warmest month maximum temperature, land use, distance from the river, soil texture, profile curvature, elevation, and topographic relief. The flash flood-prone areas in the study area varies in different periods, but are mainly distributed in Wudu District, Wen County and Tanchang County, and the simulation results for the three future periods (2041—2060, 2061—2080, 2081—2100) reflected a decreasing trend compared with the current period (2021—2040).
Flood disaster is a very destructive natural disaster. The main reasons for its generation include heavy rainfall, storm surge and dam break. When flood disaster occurs in populated areas such as cities and towns, the flood will directly threaten the safety of life and property of residents. Also it will cause paralysis of land and underground transportation, interruption of water and electricity transportation. In the process of flood rescue, quick and accurate identification of flooded roads is conducive to planning appropriate personnel transfer and material transportation routes, and reducing subsequent losses caused by floods. Aiming at the problem that roads in the flood disaster scenario cannot be automatically and correctly identified, this paper proposes an end-to-end flooded road detection method based on deep learning.
The proposed method uses a three-branch encoder-decoder structure and uses strip convolution, in which the efficient extraction of linear features is realized. And the coordination dual attention mechanism can effectively guide the network, and realize the identification of road areas. The method can effectively utilize the historical optical remote sensing images before the disaster and the real-time optical remote sensing during the disaster. The image is applied to detect the flooded and non-flooded roads in the disaster-stricken area, and a comparative experiment is carried out on the self-built dataset.
The experimental results show that the precison and recall rate are 0.838 1 and 0.666 8 on pre-disaster road, 0.796 6 and 0.607 4 on post-disaster road, 0.780 0 and 0.661 4 on affected road respectively.
The proposed method has achieved the goal of automatically identifying the flooded roads in the flood-stricken area. The ability of detecting flooded roads and non-flooded roads can provide strong support for flood disaster rescue and reduce losses of life and property caused by flood disasters.
Convolutional neural network (CNN) was widely used in landslide susceptibility assessment because of its powerful feature extraction capability. However, with the demand for scene diversification and high accuracy, the algorithm of CNN was constantly improved. The practice of improving accuracy by deepening the network hierarchy or combining other models tended to increase the number of model parameters and the amount of calculation greatly, which led to the difficulty of model training or the overfitting of results, thus limiting its practical application. We want to solve the above problems from the model structure.
We proposed to build a multi-dimensional CNN coupling model. Through the asymmetric aggregation of feature maps, one-dimensional CNN(1D-CNN) and two-dimensional CNN(2D-CNN) were connected to maintain network depth, limit model parameters, and reduce computation. The parameters sharing of multi-dimensional convolution kernels was used to capture the deep coupling features of different dimensions and different landslide factors, so as to make full use of features and avoid overfitting. Taking Sedongpu gully in Xizang,China as the experimental area, this paper selected 11 kinds of landslide influencing factors to analyze the landslide susceptibility.
The results show that the multi-dimensional CNN coupling structure had the same training efficiency as the shallow 2D-CNN with fewer parameters due to the reduced computational effort. While compared with the deep 2D-CNN with the approximate number of parameters, the training time of the proposed method was significantly reduced and the training cost was lower. In addition, the coupled model had a stronger feature learning ability than the independent 1D-CNN and 2D-CNN, and therefore obtained higher model accuracy. Under each confusion matrix metric of the testing data, the coupled model received higher scores, and thus obtained more reliable landslide susceptibility assessment results.
The multi-dimensional CNN coupling model proposed in this paper is a reliable method applicable to landslide susceptibility assessment. This study provides new theoretical guidance and technical support for further landslide hazard monitoring and prevention.
The response law of ancient (old) landslides in the reservoir area is an important research topic. Previous research primarily analyzed real-time surface displacement and reservoir water level data. However, professional monitoring conditions are often lacking on most reservoir bank slopes. This complicates tracking the landslide's historical deformation. Satellite and airborne remote sensing platforms enable multi-scale, long-term monitoring of landslide deformation and damage.
This study employs multi-source three-dimensional observation technologies including aerial, space-based, and terrestrial platforms to monitor the deformation and evolution of the Pubugou Hydropower Station's Hongyanzi landslide over approximately 10 years. It utilizes unmanned aerial vehicle photography (optical imaging) and light detection and ranging (LiDAR) for detailed topographic mapping and deformation analysis from 2009 to 2020. Additionally, time-series interferometric synthetic aperture radar (InSAR) technology is used to track long-term surface deformations from October 2014 to July 2020. Field investigations have identified typical deformation and failure characteristics of the landslide, incorporating geological conditions and external factors such as rainfall and reservoir water levels to analyze causal mechanisms and dynamic trends.
The irregularly semicircular Hongyanzi landslide spans 20-50 m in thickness, encompasses approximately 15.53 million m³ in volume, and slides at an approximate bearing of 340°. Composed of quaternary pebbled stones, silty sand, and clay, the landslide's bed slopes between 20° and 25°. Its lithology includes Emeishan Formation basalt and Yangxin Formation dolomite. Existing since 2006 or earlier, the landslide features elements like walls and steps. LiDAR imagery from 2009 clearly delineates its boundaries, though it shows no new signs of deformation or failure. Following reservoir impoundment, the reactivated landslide develops new, widening cracks along its rear edge. Post-reactivation, the landslide predominantly undergos uniform deformation, with more significant movement at the trailing edge than the leading edge, without marked acceleration. Heavy rainfall is the most significant control factor, imparting stepwise deformation characteristics to the landslide.
A comprehensive analysis of multi-source data reveals that phenomena like the Hongyanzi landslide exhibit typical long-term, gradual, and seasonal movements. Long-term InSAR effectively captures these characteristics. Multi-stage optical remote sensing and surface point cloud data from LiDAR, after vegetation removal, enable more intuitive comparisons of macroscopic deformation across different landslide areas. Integrating this with geological assessments and field investigations allows for detailed engineering analyses to ascertain the causes, patterns, and future trends of landslide activity.
The urban lifeline safety project is a major national livelihood project, which ensures the safe operation of urban gas, water, electricity and other systems. Nowadays, the project is confronted with three types of difficulties: Difficulties in mechanism discovery and experimental reproduction; difficulties in risk detection and early identification, and difficulties in accurate early warning and collaborative prevention and control. In order to reveal the mechanism and reproduce the experiments, a full-size urban lifeline multi-hazard large-scale scientific device is firstly developed in the world, a series of dis-aster modes with 5 categories and 169 kinds of disasters coupled are established, based on which a comprehensive risk dynamic assessment method coupled with underground pipelines and above-ground disaster-bearing body is presented. To improve the risk detection and early identification, highly sensitive gas sensing laser chip as well as detector with active grating and wavelength-biased are invented. We develop an intelligent ball for leakage located in water supply pipe network based on the inertial guidance, force balance, acoustic spectrum and BeiDou. Bridge overall modal (fingerprint) monitoring technology is break-through. In order to solve the problem of accurate early warning and collaborative prevention and control, an urban lifeline safety risk physical heuristic artificial intelligence early warning technology is presented, based on which a monitoring and early warning system is developed with collaborative sensing, fusion monitoring, targeting early warning, linkage response and other functions. The related studies result in the development of the safety and emergency response industry, followed by the pioneering and substantial contributions to the prevention and control of urban lifeline risks.
In mid- to late- April 2024, an extreme heavy rainfall event occurred in Shaoguan City, Guangdong Province, inducing a large number of landslides in Jiangwan Town, Shaoguan. People lost connection with the outside world for nearly 36 hours, which aroused widespread social concern. Rapidly and accurately identifying the basic characteristics of landslides, development and distribution patterns and formation conditions is crucial for disaster emergency decision-making and risk elimination and disposal.
Using the post-disaster optical remote sensing images and combining with deep learning model, the rainfall-induced landslides in Jiangwan Town, Shaoguan, were quickly and automatically identified.
After manually calibration, a total of 1 192 landslides were deciphered, with a total area of about 3.14 km². The scale of the landslides was dominated by small and medium-sized landslides, which were mainly distributed as an aggregated belt along the river in the northeast-southwest direction, with a significant characteristic of concentrated occurrence. Spatial statistical analysis showed that the landslides were mainly distributed on concave slopes with slopes of 10°-30° in the range of 200-300 m elevation. Further quantitative analysis of the geomorphic controlling factors of landslides using the random forest model and SHAP theory reveals that different topographic and geomorphic factors have different degrees of nonlinear effects on landslide formation, and that multiple factors such as elevation, slope, and catchment conditions are coupled to jointly control the formation of landslides.
This paper highlights the great advantage of deep learning-based intelligent identification and analysis technology in the emergency investigation and formation conditions analysis of landslide disasters, which can provide important technical support for the rapid assessment of disaster losses and risk identification.
The construction of effective path set is the key link of traffic control and guidance of highway network, and plays an important role in post disaster path planning. Usually when a disaster occurs, there is a problem that the current emergency route planning does not incorporate the dynamic changes of disasters in time, which affects the subsequent emergency evacuation and rescue and relief.
To solve this problem, a double-layer road network model is proposed, which couples the topological structure of the highway network with the traffic flow and the state of disasters and events. The linear reference and dynamic segmentation technology is introduced to associate the routes and events and the depth first search algorithm is improved based on Dijkstra algorithm.
(1) Double-layer road network model reflects the topological relationship between road sections, stores the dynamic attribute information of road sections and realizes the search and construction of effective path sets. (2) Improved DFS algorithm reduces the computational complexity and proposes an effective path search algorithm combining with time-varying road network, disasters and traffic conditions.
The example application and verification in the study area shows that the method can dynamically search the effective path set according to time-varying traffic conditions and disasters. This model enhances the expression and analysis ability of road data, and is able to serve traffic analysis and control under the change of disaster situation, and is suitable for the post disaster traffic operation situation assessment of road network.
Studying the spatiotemporal changes of groundwater storage(GWS) and its sustainable spatiotemporal evolution characteristics in the upper reaches of the Yellow River Basin can provide a valuable reference for the sustainable and rational development of water resources in the Yellow River Basin.
The changes of terrestrial water storage (TWS) and GWS in the upper reaches of the Yellow River Basin from April 2002 to December 2022 were estimated using Masson data and spherical harmonic (SH) data from GRACE (gravity recovery and climate experience) and GRACE-FO (GRACE follow on) gravity satellites, combined with prior hydrological models, and their temporal and spatial variation characteristics were also analyzed. The sustainability index (SI) of water resources in the study area was further calculated to evaluate the sustainability of regional groundwater. And the correlation and contribution of precipitation change, normalized difference vegetation index (NDVI), evapotranspiration (ET) and regional GWS were discussed.
The change of GWS in the study area showed an overall downward trend at a rate of approximately -3.89 ± 0.37 mm / a, and showed obvious spatial characteristics of the difference between the southern and northern values, which was consistent with the monitoring results of the measured wells (correlation was approximately 0.73). During the study period, the regional groundwater was almost in a state of severe unsustainability, and the spatial sustainability also gradually decreased from south to north, with an average sustainability index of only 0.38. The size of the contribution measure shows that NDVI has the greatest impact on the change of GWS in the study area, ET has the second highest impact,and the rainfall has the smallest; there was a significant negative correlation between NDVI, ET and regional GWS changes (correlation coefficients were approximately -0.76 and -0.77, respectively). The rainfall in the south and north of the study area was positively correlated with the corresponding GWS changes (correlation coefficients were approximately 0.54 and 0.50, respectively).
This study effectively evaluated the changes of TWS and GWS in the upper reaches of the Yellow River Basin over the past 20 years, and reasonably evaluated the spatiotemporal distribution characteristics of groundwater sustainability in the upper reaches of the Yellow River, as well as the correlation between external influences and GWS.
In order to improve the efficiency of landslide extraction and explore the spatial-temporal distribution characteristics of regional landslides and single landslide, a rapid landslide extraction model is designed to provide a scientific basis for landslide disaster prevention and management.
Based on multi-temporal domestic high-resolution remote sensing satellite images, advanced land observing satellite 12.5 m digital elevation model, and historical landslide hidden danger points, this study selected 8 landslide-prone townships located in the northwest of Fengjie County, Chongqing city,China to form the study area. Feature optimization based on SHAP in terpretation framework and Bayesian hyperparameter automatic optimization based on Optuna framework are introduced into XGBoost algorithm to construct and optimize the landslide extraction model. The study realized rapid extraction of landslide spatial information and quantitative analysis of landslide spatialtemporal distribution in 2013, 2015, 2018 and 2020.
In the comparison of models accuracy, precision, Kappa coefficient and area under curve value of landslide extraction model constructed by optimized XGBoost basic algorithm are 96.26%, 90.91%, 0.860 2 and 0.970 5, respectively, which higher than GBDT, LightGBM and Adaboost.
From 2013 to 2020, the overall development degree of landslides in the study area is relatively high, and the spatial distribution of landslides is uneven among villages and towns, and the landslides is distributed on both sides of the river valley and the river, showing the characteristics of regional concentration. From 2013 to 2020, Miaowan landslide has high development intensity, strong activity, repeated resurrection phenomenon and induced new landslides. The slope of the landslide is about 25°-45°, and it’s topographic characteristics change little, significant changes mainly focus on color, texture, geometry and vegetation coverage.
Pixel offset tracking (POT) for optical remote sensing imagery is widely used to invert coseismic deformation fields and monitor landslides. Traditional pixel offset tracking method estimates the displacement of the central pixel by searching for the matching window with the highest correlation, which is computationally inefficient and suffers from inaccurate deformation boundary extraction due to the decoherence effects in the region with dynamical deformation. We introduce the optical flow field model commonly used in computer vision to the pixel offset tracking problem to obtain accurate surface deformation efficiently.
The optical flow field method applicable to optical remote sensing images and the improved inversion algorithm for the time series analysis are proposed to inverse the surface deformation. Experiments on the simulated coseismic deformation fields in Tajikistan are detailed to assess the feasibility and the minimum detectable deformation of the optical flow field method. The advantages of the proposed method over computational cost and deformation boundary extraction accuracy are illustrated by the co-seismic deformation field of the California earthquake and the displacement of the Baige landslide. Furthermore, the performance on estimating large gradient deformation and the robustness of the improved time series inversion algorithm are discussed by analyzing the time series deformation of the Baige landslide.
The results show that compared with the traditional window correlation matching method, the optical flow field method has an offset tracking accuracy of 0.032 pixel, which improves the computational efficiency by about 20 times, and the accuracy of the deformation zone is improved by 25.9%. The time series weighted inversion algorithm reduces the uncertainties in the estimation of east-west and north-south displacements of optical remote sensing images by 16.2% and 12.4%, respectively.
The proposed method alleviates the pixel offset tracking problem in the boundary region with large gradient deformation.
Mine slope instability is one of the main factors restricting the safety production of open-pit mines in China. Ground-based synthetic aperture radar interferometry technology has been gradually introduced into the application of slope safety monitoring and early warning prediction in open-pit mines. However, the high-frequency rolling update characteristics of ground radar interferometry data lead to large data error accumulation and unobvious curve mutation characteristics.
Processing the original data by dislocation subtraction and velocity reciprocal method can effectively reduce the vibration of high-frequency data, improve the readability of critical sliding data. After data processing, it can highlight the trend characteristics of key deformation data. The research is based on the analysis of cumulative displacement curve, velocity curve and reciprocal velocity curve group treated with different periods.
It is found that there are three characteristic points in the curve group: Sudden deformation increase point, velocity increase point and stable vibration point. Through these characteristic points, the slope landslide disaster can be predicted. The trend of key deformation data can be highlighted by using the three feature points of deformation sudden increase point, velocity growth point and stable vibration point.
Through the identification of three feature points, the possible landslide can be effectively identified in advance and the landslide time can be predicted, which provides a new technical path and solution for landslide early warning and prediction analysis based on ground-based interferometric radar.
Land subsidence caused by long-term over-exploitation of groundwater is one of major problems in Beijing. Since the opening of the South-to-North Water Transfer Project, the problem of water shortage in Beijing has been greatly alleviated, and the pressure of land subsidence has been reduced to a certain extent.
In order to analyze the development of land subsidence after the start of the South-to-North Water Transfer in Beijing, ascending and descending time-series interferometric synthetic aperture radar (InSAR) technique is used to monitor land subsidence in Beijing. First, the mean deformation velocity and cumulative deformation in line of sight in Beijing from January 2015 to December 2020 is obtained by the small baseline subset InSAR. Second, the robust least square fitting method is used to fuse the deformation results of the lifting rail, after that the global positioning system monitoring data are compared with the fusion results of lifting rail. Finally, the variation trend between the deformation results obtained by the robust least quadratic fitting and groundwater data is analyzed.
The deformation results show that the center of Beijing is basically stable and the deformation distribution is not uniform. The maximum ascending annual deformation velocity and the maximum ascending cumulative deformation amount reach -134 mm/a and -697 mm respectively. The maximum descending annual deformation velocity and the maximum descending cumulative deformation amount reach -135 mm/a and -734 mm respectively. And the fusion results obtained by the least square fitting method has reliability and accuracy.
The subsidence rate in Beijing shows a decreasing trend with the gradual increase of groundwater level. In general, the middle route of South-to-North Water Transfer Project has alleviated the expansion trend of land subsidence in Beijing to a certain extent.
In the research work of reservoir landslide displacement prediction, due to the lag of reservoir water level response, it is difficult for the traditional landslide displacement prediction model to analyze the monotonically increasing step deformation characteristics, which seriously affects the prediction results, and it is necessary to establish a landslide displacement prediction model that can consider the time lag effect.
We analyze the time lag effect of reservoir levels separately through grey correlation, account for the cumulative effect of earlier rainfall, and consider the effect of earthquake on landslide deformation, and establishe an autoregressive distributed lag landslide displacement prediction model that can be applied to engineering sites.
The results show that: (1) The engineering case study concluded that rising reservoir levels and earthquakes were the main triggering factors for the increased deformation of the landslide, and the lag time of reservoir levels acting on landslide deformation was 8 days. (2) The correlation coefficient between the cumulative landslide displacement and the actual displacement calculated by the new model is as high as 0.992 7, with a root mean square error of 14.11 mm. (3) The calculation of trend speed ratio indicators can provide a new sensitivity evaluation parameter for landslide monitoring and early warning.
The study establishes a physically significant prediction model for reservoir bank landslide displacements, provides a comprehensive analysis of landslide displacements, achieves a quantitative calculation of the seismic contribution to landslide displacement evolution, and provides new technical support for the safety risk management of the whole process of reservoir bank landslide evolution during the water storage period.
As a continuation of mining subsidence, the surface secondary subsidence (including sinking and uplift) of closed mines poses a potential threat to the safety of surface buildings-structures in mining areas. However, at this stage, the law analysis of the surface secondary subsidence of closed mines under different geological mining conditions is not yet comprehensive. Due to the high underground water level, thick Quaternary loose layers, and multiple coal seams in the Huainan mining area, monitoring and analyzing the surface secondary subsidence of closed mines in the Huainan mining area has important theoretical and practical values.
First, in order to verify the reliability of the monitoring results, the StaMPS software is employed to simultaneously perform persistent scatterers interferometry and small baseline subset processing. Amplitude dispersion index and amplitude difference dispersion index are used to select coherent points, respectively. Then, the unwrapped phases of the coherent points are obtained by a three-dimensional phase unwrapping algorithm. Finally, the surface subsidence of the coherent points are obtained by using temporal-domain low-pass and spatial-domain high-pass filtering.
The results show that: (1) The surface uplifts after the closure of Xinjisan and Lizuizi-Xinzhuangzi-Xieyi mines, with a maximum uplift rate of 51.1 mm/a, and is located in Xinzhuangzi mine, while the surface of Panyi mine is still sinking due to its late closure in September 2018. (2) The surface secondary subsidence law of closed mines in Huainan mining area is sinking stage⁃stable stage⁃uplift stage, which is consistent with the surface subsidence law of closed mines in Xuzhou mining area, but the sinking, stable and uplift stages do not necessarily occur successively over time. (3) There may be a hydraulic connection between Lizuizi, Xinzhuangzi, and Xieyi mines. The groundwater first rises from the junction of Xinzhuangzi and Xieyi mines and then flows to the southeast and northwest sides.
Although the law of surface subsidence in the closed mines of Huainan and Xuzhou mines is relatively consistent, there are differences in the law of surface uplift due to different hydrogeological mining conditions. Therefore, in future work, we will continue to pay attention to the law of surface secondary subsidence of closed mines under different geological mining conditions.
The landslides disaster is one of the most important factors for the construction of major infrastructure in the western China. How to effectively and reliably carry out wide-area landslide susceptibility prediction has always been a frontier difficulty in domestic and foreign studies. However, the present data-driven methods for prediction of landslide susceptibility in large-scale and complex scenarios still face two major issues:(1) The difference between various landslide inducing environments in a wide range of scenarios, would cause the difficulty to applying a single model to account for multiple landslide phenomenon;(2) small sample problem: Complex environmental tasks require models with large capacity and strong representative power, but there is a lack of sufficient landslide samples in practice.
In response to the above problems, this paper takes Qijiang and Fuling District of Chongqing City, China as an example, proposes a local prediction strategy, and introduces the idea of meta-training an intermediate representation suited to be generalized, that can be adapted to the landslide sensitivity prediction task cor-responding to the current local area, with only a very small number of samples in the local area, and number of iterations. Thus, the two issues mentioned can be well settled.
The proposed method is different from traditional methods such as support vector machines, multilayer perceptrons, and random forests, which require a large number of samples and gradient iterations to train the supervised model. Instead, only a small sample is required to fine-tune the intermediate model, which still improves the global accuracy by 1%-5%, the precision by 1%-3%, the
The meta-learning paradigm enables few-shot adaptation of landslide susceptibility prediction model with superior statistical performance.
With the development of Shenzhen city, China, land renovation is more frequent. At the same time, affected by the subtropical monsoon climate, the area under the jurisdiction has abundant rainfall and dense vegetation coverage, making it difficult to identify the hidden dangers of geological hazards widely distributed on artificial slopes and natural slopes. Therefore, it is necessary to develop a set of hazard evaluation system of geological disaster that can solve the unique terrain and climate conditions in Shenzhen, so as to achieve the purpose of preventing disasters in advance and reducing casualties.
(1) On the basis of high-precision digital elevation model of Shenzhen city obtained by airborne light detection and ranging(LiDAR), about 3 500 slope disaster prone points in Shenzhen are obtained through data collection, remote sensing interpretation and field verification. The sample library expanded 330% after proofreading.(2) Taking 3 major factors (8 factors) of terrain, geological structure and human engineering activities into comprehensive consideration, and based on the rainfall-induced disaster mechanism, a rainfall collection factor is proposed, and the weight of evidence method is used to complete the geological disaster hazard evaluation model under rainfall-induced conditions.(3) The threshold determination method of“key point control”under the actual background of single disaster is proposed, and the classification of the risk assessment model is completed.
The area under curve value of receiver operating characteristic curve model reaches 0.903, indicating that the model has a good effect on disaster forecasting. LiDAR technology can improve the identification accuracy of geological hazards in cities under dense vegetation coverage.
Based on airborne LiDAR data, through a series of means such as expansion of disaster database, analysis of disaster distribution law, establishment of disaster evaluation factors, and classification of risk levels, it can form a refined evaluation system for the hazard evaluation of the slope in densely vegetated areas under the influence of the subtropical monsoon climate.
Because of high frequency of extreme weather, railway subgrade disaster shows a trend of increasing, great harmfulness and is hard to detect in advance. Even a small-size subgrade disaster may cause railway paralysis, which prevents the development of transportation. Many incidents illustrate that the earliest subgrade disaster always appeared in areas where no case ever reported before. It means that traditional monitoring methods are hard to detect hidden danger area. Thus, a promising method is urgent to be proposed for meeting the monitoring requirement for railway safety.
First, the characteristics of subgrade disaster are summarized under different disaster-pregnant environments, and the advantages of various monitoring methods are discussed to find a collaborative applications method for early identification of subgrade disaster. The potential hazards that may cause damage to subgrade structure and the deterioration degree of subgrade are considered as the main monitoring object of subgrade service status, including subgrade deformation monitoring, structural health monitoring, geological hazard monitoring along the railway, track irregularity, and external environmental monitoring. Second, an investigative approach based on the integration of space-air-train-ground muti-source techniques is proposed to detect the geohazards and monitor the service status of railway subgrade. It means that collaborative application of different monitoring methods, cooperative analysis of different scales and resolutions data and collaboration of between various railway departments are required. Two monitoring schemes for subgrade disasters and service status of subgrade are proposed based on the integration of multi-source and multi-scale monitoring technique. Finally, the development direction of subgrade service status monitoring is discussed.
This monitoring system has been applied in the identification of subgrade service status in the Shanghai-Nanjing high speed railway, which can quickly investigate the location of the disasters and the deterioration degree of subgrade.
On September 20, 2019, the Jungong ancient landslide in Lajia Town, Maqin County, Qinghai Province, China suffered a local failure, which led to the interruption of traffic and seriously threatened the safety of local residents' lives and properties. It is urgent to find out the deformation area and deformation law of the ancient landslide, and to provide the support for prevention and control enginee-ring design, monitoring and early warning.
First, using high-resolution satellite images, digital elevation model data and combined with field investigations, the ancient landslide were zoned based on landslide morphological characteristics and signs of deformations; further, Sentinel-1 radar satellite descending data from January 2017 to December 2020 were used to analyze the surface deformation characte-ristics and deformation patterns of the ancient landslide based on small baseline subset interferometric synthetic aperture radar technology (SBAS-InSAR).
Based on the morphological characteristics and deformation signs of the landslide, the ancient landslide was divided into four sub-areas. The SBAS-InSAR results show that the ancient landslide is in a continuous slow creeping state. The strong deformation area of the landslide is mainly located in the road excavation section. Human activities greatly disturb the stability of the ancient landslide. The deformation rate of the strong deformation area of the landslide has a good relationship with rainfall.
Although the ancient landslide has been partially treated with anti-slide piles and other projects, the ancient landslide has multi-level sliding surfaces, and the depth of the existing anti-slide piles is not enough. Although the ancient landslide has played a certain role in anti-slide, it has not completely prevented the creep deformation of the landslide. It is suggested that the subsequent treatment projects should use drilling and other exploration techniques to find out the depth of the multi-level sliding surfaces before designing, and installing on-site real-time monitoring and early warning equipment such as crack meters in the strongly deformed areas. Combined with the medium and long-term monitoring of radar satellite InSAR, a point-surface monitoring and early warning system could be realized.
The continuous exploitation of the Liaohe Delta oilfield has resulted in severe surface subsidence, impacting oil recovery rates, production operations, and posing threats to surface infrastructure and ecological environments. To ensure the safe exploitation of underground fluid resources and protect the regional environment, monitoring implementation is needed for this region.
The method of small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technology with coherence information as weights was used to analyze the surface deformation. Using the fusion decomposition of synthetic aperture radar descending and ascending orbit results to extract both vertical and horizontal east-west deformation within the Shuguang oilfield area. Subsequently, a reservoir compaction-induced subsidence inversion model is applied to the Shuguang oilfield to simulate and interpret the observed subsidence phenomena, linking them to the underground fluid resource exploitation activities.
The results reveal significant ground subsidence throughout the Liaohe Delta region, particularly in the Shuguang oilfield and Huanxiling oilfield. The average line of sight subsidence rates reaching 158 mm/a and 73 mm/a, respectively. In the Shuguang oilfield, there is horizontal movement towards the subsidence center, with approximately equal magnitudes of movement on the east and west sides. The maximum horizontal movement rate is observed to be -62 mm/a (westward motion). Furthermore, the reservoir compaction and subsidence model based on Shuguang oilfield reservoir parameters effectively invert the maximum subsidence position at the center of the oilfield, with subsidence range and magnitude consistent with the InSAR observation results.
The study concludes that continuous exploitation of oil has led to significant ground subsidence in the Liaohe Delta region, especially in Shuguang oilfield and Huanxiling oilfield, with clear patterns of subsidence and horizontal movement detected using SBAS-InSAR technology. The developed reservoir compaction-induced subsidence inversion model proves effective in simulating subsidence phenomena associated with oilfield operations. These findings underscore the importance of monitoring and managing subsidence risks to ensure the safe exploitation of underground resources and to protect regional ecological environments in the Liaohe Delta region.
Geological hazard points and hidden danger points are the data basis for geological hazard evaluation, while the existing records of geological hazard points have poor timeliness and are incomplete. To solve this problem, the deformation information obtained by multi-temporal interferometric synthetic aperture radar (InSAR) was integrated into the geological hazard evaluation model. And we explore how to make better use of the deformation information.
The greater the deformation level, the greater the possibility of geological hazards. This paper not only takes the deformation points obtained by multi-temporal InSAR as the geological hazard points/hidden danger points, but also integrates the deformation level information obtained by multi-temporal InSAR as an evaluation factor into the susceptibility evaluation model, making full use of the effective deformation information obtained by multi-temporal InSAR. And the evaluation model adopts the coupling model based on information value model and the analytic hierarchy process model to obtain the susceptibility evaluation and zoning of the geological hazards in Baiyin City, Gansu Province,China.
Through the verification of the existing geological disaster point data, it is found that the partitions in this paper are in good agreement with the existing geological hazard points distribution.In the designated extremely high-prone areas, there are nearly 8 geological disaster points within 10 km2, while less than one in the extremely low-prone areas.
The multi-temporal InSAR deformation information added to the geological hazard evaluation model greatly improves the timeliness and quantity of records of geological hazard points/hidden points. However, only one kind of synthetic aperture radar data cannot completely identify all geological hazard points/hidden danger points, due to the limitations of incidence angle and microwave wavelength. In the futher work, we will focus on the combination of multiple deformation monitoring technologies to jointly monitor surface deformation, such as multi-sensor and multi-track InSAR technology, airborne light laser detection and ranging and high-resolution optical remote sensing.
Flash floods are natural disasters caused by sudden rise in water levels in mountainous rivers, which are characterized by instantaneity and great destructiveness. In recent years, the frequent occurrence of flash floods in Longnan city, Gansu province, has posed a serious threat to the safety of local people's lives and property, thus it is urgent to carry out a risk assessment of flash floods in this region.
This study takes Longnan city as the study area, and utilizes the MaxEnt model combining with the particle swarm algorithm to evaluate the vulnerability of study area based on 834 flash flood hazard points investigated and 32 disaster-causing factors. It also predicts the spatial pattern changes and potential mass migration trends of the future flash flood vulnerability areas based on three periods of climate data from the current period (2021—2040) and the future period (2041—2060, 2061—2080, 2081—2100).
The area under receiver operating characteristic curve of the results of the study in each period is above 0.85, which indicates that the precision of the results of the method is good. The main cause factors in this study area are driest month precipitation, monthly mean diurnal temperature difference, coefficient of variation of precipitation, warmest month maximum temperature, land use, distance from the river, soil texture, profile curvature, elevation, and topographic relief. The flash flood-prone areas in the study area varies in different periods, but are mainly distributed in Wudu District, Wen County and Tanchang County, and the simulation results for the three future periods (2041—2060, 2061—2080, 2081—2100) reflected a decreasing trend compared with the current period (2021—2040).
Flood disaster is a very destructive natural disaster. The main reasons for its generation include heavy rainfall, storm surge and dam break. When flood disaster occurs in populated areas such as cities and towns, the flood will directly threaten the safety of life and property of residents. Also it will cause paralysis of land and underground transportation, interruption of water and electricity transportation. In the process of flood rescue, quick and accurate identification of flooded roads is conducive to planning appropriate personnel transfer and material transportation routes, and reducing subsequent losses caused by floods. Aiming at the problem that roads in the flood disaster scenario cannot be automatically and correctly identified, this paper proposes an end-to-end flooded road detection method based on deep learning.
The proposed method uses a three-branch encoder-decoder structure and uses strip convolution, in which the efficient extraction of linear features is realized. And the coordination dual attention mechanism can effectively guide the network, and realize the identification of road areas. The method can effectively utilize the historical optical remote sensing images before the disaster and the real-time optical remote sensing during the disaster. The image is applied to detect the flooded and non-flooded roads in the disaster-stricken area, and a comparative experiment is carried out on the self-built dataset.
The experimental results show that the precison and recall rate are 0.838 1 and 0.666 8 on pre-disaster road, 0.796 6 and 0.607 4 on post-disaster road, 0.780 0 and 0.661 4 on affected road respectively.
The proposed method has achieved the goal of automatically identifying the flooded roads in the flood-stricken area. The ability of detecting flooded roads and non-flooded roads can provide strong support for flood disaster rescue and reduce losses of life and property caused by flood disasters.
Convolutional neural network (CNN) was widely used in landslide susceptibility assessment because of its powerful feature extraction capability. However, with the demand for scene diversification and high accuracy, the algorithm of CNN was constantly improved. The practice of improving accuracy by deepening the network hierarchy or combining other models tended to increase the number of model parameters and the amount of calculation greatly, which led to the difficulty of model training or the overfitting of results, thus limiting its practical application. We want to solve the above problems from the model structure.
We proposed to build a multi-dimensional CNN coupling model. Through the asymmetric aggregation of feature maps, one-dimensional CNN(1D-CNN) and two-dimensional CNN(2D-CNN) were connected to maintain network depth, limit model parameters, and reduce computation. The parameters sharing of multi-dimensional convolution kernels was used to capture the deep coupling features of different dimensions and different landslide factors, so as to make full use of features and avoid overfitting. Taking Sedongpu gully in Xizang,China as the experimental area, this paper selected 11 kinds of landslide influencing factors to analyze the landslide susceptibility.
The results show that the multi-dimensional CNN coupling structure had the same training efficiency as the shallow 2D-CNN with fewer parameters due to the reduced computational effort. While compared with the deep 2D-CNN with the approximate number of parameters, the training time of the proposed method was significantly reduced and the training cost was lower. In addition, the coupled model had a stronger feature learning ability than the independent 1D-CNN and 2D-CNN, and therefore obtained higher model accuracy. Under each confusion matrix metric of the testing data, the coupled model received higher scores, and thus obtained more reliable landslide susceptibility assessment results.
The multi-dimensional CNN coupling model proposed in this paper is a reliable method applicable to landslide susceptibility assessment. This study provides new theoretical guidance and technical support for further landslide hazard monitoring and prevention.
The response law of ancient (old) landslides in the reservoir area is an important research topic. Previous research primarily analyzed real-time surface displacement and reservoir water level data. However, professional monitoring conditions are often lacking on most reservoir bank slopes. This complicates tracking the landslide's historical deformation. Satellite and airborne remote sensing platforms enable multi-scale, long-term monitoring of landslide deformation and damage.
This study employs multi-source three-dimensional observation technologies including aerial, space-based, and terrestrial platforms to monitor the deformation and evolution of the Pubugou Hydropower Station's Hongyanzi landslide over approximately 10 years. It utilizes unmanned aerial vehicle photography (optical imaging) and light detection and ranging (LiDAR) for detailed topographic mapping and deformation analysis from 2009 to 2020. Additionally, time-series interferometric synthetic aperture radar (InSAR) technology is used to track long-term surface deformations from October 2014 to July 2020. Field investigations have identified typical deformation and failure characteristics of the landslide, incorporating geological conditions and external factors such as rainfall and reservoir water levels to analyze causal mechanisms and dynamic trends.
The irregularly semicircular Hongyanzi landslide spans 20-50 m in thickness, encompasses approximately 15.53 million m³ in volume, and slides at an approximate bearing of 340°. Composed of quaternary pebbled stones, silty sand, and clay, the landslide's bed slopes between 20° and 25°. Its lithology includes Emeishan Formation basalt and Yangxin Formation dolomite. Existing since 2006 or earlier, the landslide features elements like walls and steps. LiDAR imagery from 2009 clearly delineates its boundaries, though it shows no new signs of deformation or failure. Following reservoir impoundment, the reactivated landslide develops new, widening cracks along its rear edge. Post-reactivation, the landslide predominantly undergos uniform deformation, with more significant movement at the trailing edge than the leading edge, without marked acceleration. Heavy rainfall is the most significant control factor, imparting stepwise deformation characteristics to the landslide.
A comprehensive analysis of multi-source data reveals that phenomena like the Hongyanzi landslide exhibit typical long-term, gradual, and seasonal movements. Long-term InSAR effectively captures these characteristics. Multi-stage optical remote sensing and surface point cloud data from LiDAR, after vegetation removal, enable more intuitive comparisons of macroscopic deformation across different landslide areas. Integrating this with geological assessments and field investigations allows for detailed engineering analyses to ascertain the causes, patterns, and future trends of landslide activity.
The urban lifeline safety project is a major national livelihood project, which ensures the safe operation of urban gas, water, electricity and other systems. Nowadays, the project is confronted with three types of difficulties: Difficulties in mechanism discovery and experimental reproduction; difficulties in risk detection and early identification, and difficulties in accurate early warning and collaborative prevention and control. In order to reveal the mechanism and reproduce the experiments, a full-size urban lifeline multi-hazard large-scale scientific device is firstly developed in the world, a series of dis-aster modes with 5 categories and 169 kinds of disasters coupled are established, based on which a comprehensive risk dynamic assessment method coupled with underground pipelines and above-ground disaster-bearing body is presented. To improve the risk detection and early identification, highly sensitive gas sensing laser chip as well as detector with active grating and wavelength-biased are invented. We develop an intelligent ball for leakage located in water supply pipe network based on the inertial guidance, force balance, acoustic spectrum and BeiDou. Bridge overall modal (fingerprint) monitoring technology is break-through. In order to solve the problem of accurate early warning and collaborative prevention and control, an urban lifeline safety risk physical heuristic artificial intelligence early warning technology is presented, based on which a monitoring and early warning system is developed with collaborative sensing, fusion monitoring, targeting early warning, linkage response and other functions. The related studies result in the development of the safety and emergency response industry, followed by the pioneering and substantial contributions to the prevention and control of urban lifeline risks.
In mid- to late- April 2024, an extreme heavy rainfall event occurred in Shaoguan City, Guangdong Province, inducing a large number of landslides in Jiangwan Town, Shaoguan. People lost connection with the outside world for nearly 36 hours, which aroused widespread social concern. Rapidly and accurately identifying the basic characteristics of landslides, development and distribution patterns and formation conditions is crucial for disaster emergency decision-making and risk elimination and disposal.
Using the post-disaster optical remote sensing images and combining with deep learning model, the rainfall-induced landslides in Jiangwan Town, Shaoguan, were quickly and automatically identified.
After manually calibration, a total of 1 192 landslides were deciphered, with a total area of about 3.14 km². The scale of the landslides was dominated by small and medium-sized landslides, which were mainly distributed as an aggregated belt along the river in the northeast-southwest direction, with a significant characteristic of concentrated occurrence. Spatial statistical analysis showed that the landslides were mainly distributed on concave slopes with slopes of 10°-30° in the range of 200-300 m elevation. Further quantitative analysis of the geomorphic controlling factors of landslides using the random forest model and SHAP theory reveals that different topographic and geomorphic factors have different degrees of nonlinear effects on landslide formation, and that multiple factors such as elevation, slope, and catchment conditions are coupled to jointly control the formation of landslides.
This paper highlights the great advantage of deep learning-based intelligent identification and analysis technology in the emergency investigation and formation conditions analysis of landslide disasters, which can provide important technical support for the rapid assessment of disaster losses and risk identification.
The construction of effective path set is the key link of traffic control and guidance of highway network, and plays an important role in post disaster path planning. Usually when a disaster occurs, there is a problem that the current emergency route planning does not incorporate the dynamic changes of disasters in time, which affects the subsequent emergency evacuation and rescue and relief.
To solve this problem, a double-layer road network model is proposed, which couples the topological structure of the highway network with the traffic flow and the state of disasters and events. The linear reference and dynamic segmentation technology is introduced to associate the routes and events and the depth first search algorithm is improved based on Dijkstra algorithm.
(1) Double-layer road network model reflects the topological relationship between road sections, stores the dynamic attribute information of road sections and realizes the search and construction of effective path sets. (2) Improved DFS algorithm reduces the computational complexity and proposes an effective path search algorithm combining with time-varying road network, disasters and traffic conditions.
The example application and verification in the study area shows that the method can dynamically search the effective path set according to time-varying traffic conditions and disasters. This model enhances the expression and analysis ability of road data, and is able to serve traffic analysis and control under the change of disaster situation, and is suitable for the post disaster traffic operation situation assessment of road network.
Studying the spatiotemporal changes of groundwater storage(GWS) and its sustainable spatiotemporal evolution characteristics in the upper reaches of the Yellow River Basin can provide a valuable reference for the sustainable and rational development of water resources in the Yellow River Basin.
The changes of terrestrial water storage (TWS) and GWS in the upper reaches of the Yellow River Basin from April 2002 to December 2022 were estimated using Masson data and spherical harmonic (SH) data from GRACE (gravity recovery and climate experience) and GRACE-FO (GRACE follow on) gravity satellites, combined with prior hydrological models, and their temporal and spatial variation characteristics were also analyzed. The sustainability index (SI) of water resources in the study area was further calculated to evaluate the sustainability of regional groundwater. And the correlation and contribution of precipitation change, normalized difference vegetation index (NDVI), evapotranspiration (ET) and regional GWS were discussed.
The change of GWS in the study area showed an overall downward trend at a rate of approximately -3.89 ± 0.37 mm / a, and showed obvious spatial characteristics of the difference between the southern and northern values, which was consistent with the monitoring results of the measured wells (correlation was approximately 0.73). During the study period, the regional groundwater was almost in a state of severe unsustainability, and the spatial sustainability also gradually decreased from south to north, with an average sustainability index of only 0.38. The size of the contribution measure shows that NDVI has the greatest impact on the change of GWS in the study area, ET has the second highest impact,and the rainfall has the smallest; there was a significant negative correlation between NDVI, ET and regional GWS changes (correlation coefficients were approximately -0.76 and -0.77, respectively). The rainfall in the south and north of the study area was positively correlated with the corresponding GWS changes (correlation coefficients were approximately 0.54 and 0.50, respectively).
This study effectively evaluated the changes of TWS and GWS in the upper reaches of the Yellow River Basin over the past 20 years, and reasonably evaluated the spatiotemporal distribution characteristics of groundwater sustainability in the upper reaches of the Yellow River, as well as the correlation between external influences and GWS.
In order to improve the efficiency of landslide extraction and explore the spatial-temporal distribution characteristics of regional landslides and single landslide, a rapid landslide extraction model is designed to provide a scientific basis for landslide disaster prevention and management.
Based on multi-temporal domestic high-resolution remote sensing satellite images, advanced land observing satellite 12.5 m digital elevation model, and historical landslide hidden danger points, this study selected 8 landslide-prone townships located in the northwest of Fengjie County, Chongqing city,China to form the study area. Feature optimization based on SHAP in terpretation framework and Bayesian hyperparameter automatic optimization based on Optuna framework are introduced into XGBoost algorithm to construct and optimize the landslide extraction model. The study realized rapid extraction of landslide spatial information and quantitative analysis of landslide spatialtemporal distribution in 2013, 2015, 2018 and 2020.
In the comparison of models accuracy, precision, Kappa coefficient and area under curve value of landslide extraction model constructed by optimized XGBoost basic algorithm are 96.26%, 90.91%, 0.860 2 and 0.970 5, respectively, which higher than GBDT, LightGBM and Adaboost.
From 2013 to 2020, the overall development degree of landslides in the study area is relatively high, and the spatial distribution of landslides is uneven among villages and towns, and the landslides is distributed on both sides of the river valley and the river, showing the characteristics of regional concentration. From 2013 to 2020, Miaowan landslide has high development intensity, strong activity, repeated resurrection phenomenon and induced new landslides. The slope of the landslide is about 25°-45°, and it’s topographic characteristics change little, significant changes mainly focus on color, texture, geometry and vegetation coverage.
Pixel offset tracking (POT) for optical remote sensing imagery is widely used to invert coseismic deformation fields and monitor landslides. Traditional pixel offset tracking method estimates the displacement of the central pixel by searching for the matching window with the highest correlation, which is computationally inefficient and suffers from inaccurate deformation boundary extraction due to the decoherence effects in the region with dynamical deformation. We introduce the optical flow field model commonly used in computer vision to the pixel offset tracking problem to obtain accurate surface deformation efficiently.
The optical flow field method applicable to optical remote sensing images and the improved inversion algorithm for the time series analysis are proposed to inverse the surface deformation. Experiments on the simulated coseismic deformation fields in Tajikistan are detailed to assess the feasibility and the minimum detectable deformation of the optical flow field method. The advantages of the proposed method over computational cost and deformation boundary extraction accuracy are illustrated by the co-seismic deformation field of the California earthquake and the displacement of the Baige landslide. Furthermore, the performance on estimating large gradient deformation and the robustness of the improved time series inversion algorithm are discussed by analyzing the time series deformation of the Baige landslide.
The results show that compared with the traditional window correlation matching method, the optical flow field method has an offset tracking accuracy of 0.032 pixel, which improves the computational efficiency by about 20 times, and the accuracy of the deformation zone is improved by 25.9%. The time series weighted inversion algorithm reduces the uncertainties in the estimation of east-west and north-south displacements of optical remote sensing images by 16.2% and 12.4%, respectively.
The proposed method alleviates the pixel offset tracking problem in the boundary region with large gradient deformation.
Mine slope instability is one of the main factors restricting the safety production of open-pit mines in China. Ground-based synthetic aperture radar interferometry technology has been gradually introduced into the application of slope safety monitoring and early warning prediction in open-pit mines. However, the high-frequency rolling update characteristics of ground radar interferometry data lead to large data error accumulation and unobvious curve mutation characteristics.
Processing the original data by dislocation subtraction and velocity reciprocal method can effectively reduce the vibration of high-frequency data, improve the readability of critical sliding data. After data processing, it can highlight the trend characteristics of key deformation data. The research is based on the analysis of cumulative displacement curve, velocity curve and reciprocal velocity curve group treated with different periods.
It is found that there are three characteristic points in the curve group: Sudden deformation increase point, velocity increase point and stable vibration point. Through these characteristic points, the slope landslide disaster can be predicted. The trend of key deformation data can be highlighted by using the three feature points of deformation sudden increase point, velocity growth point and stable vibration point.
Through the identification of three feature points, the possible landslide can be effectively identified in advance and the landslide time can be predicted, which provides a new technical path and solution for landslide early warning and prediction analysis based on ground-based interferometric radar.
Land subsidence caused by long-term over-exploitation of groundwater is one of major problems in Beijing. Since the opening of the South-to-North Water Transfer Project, the problem of water shortage in Beijing has been greatly alleviated, and the pressure of land subsidence has been reduced to a certain extent.
In order to analyze the development of land subsidence after the start of the South-to-North Water Transfer in Beijing, ascending and descending time-series interferometric synthetic aperture radar (InSAR) technique is used to monitor land subsidence in Beijing. First, the mean deformation velocity and cumulative deformation in line of sight in Beijing from January 2015 to December 2020 is obtained by the small baseline subset InSAR. Second, the robust least square fitting method is used to fuse the deformation results of the lifting rail, after that the global positioning system monitoring data are compared with the fusion results of lifting rail. Finally, the variation trend between the deformation results obtained by the robust least quadratic fitting and groundwater data is analyzed.
The deformation results show that the center of Beijing is basically stable and the deformation distribution is not uniform. The maximum ascending annual deformation velocity and the maximum ascending cumulative deformation amount reach -134 mm/a and -697 mm respectively. The maximum descending annual deformation velocity and the maximum descending cumulative deformation amount reach -135 mm/a and -734 mm respectively. And the fusion results obtained by the least square fitting method has reliability and accuracy.
The subsidence rate in Beijing shows a decreasing trend with the gradual increase of groundwater level. In general, the middle route of South-to-North Water Transfer Project has alleviated the expansion trend of land subsidence in Beijing to a certain extent.
In the research work of reservoir landslide displacement prediction, due to the lag of reservoir water level response, it is difficult for the traditional landslide displacement prediction model to analyze the monotonically increasing step deformation characteristics, which seriously affects the prediction results, and it is necessary to establish a landslide displacement prediction model that can consider the time lag effect.
We analyze the time lag effect of reservoir levels separately through grey correlation, account for the cumulative effect of earlier rainfall, and consider the effect of earthquake on landslide deformation, and establishe an autoregressive distributed lag landslide displacement prediction model that can be applied to engineering sites.
The results show that: (1) The engineering case study concluded that rising reservoir levels and earthquakes were the main triggering factors for the increased deformation of the landslide, and the lag time of reservoir levels acting on landslide deformation was 8 days. (2) The correlation coefficient between the cumulative landslide displacement and the actual displacement calculated by the new model is as high as 0.992 7, with a root mean square error of 14.11 mm. (3) The calculation of trend speed ratio indicators can provide a new sensitivity evaluation parameter for landslide monitoring and early warning.
The study establishes a physically significant prediction model for reservoir bank landslide displacements, provides a comprehensive analysis of landslide displacements, achieves a quantitative calculation of the seismic contribution to landslide displacement evolution, and provides new technical support for the safety risk management of the whole process of reservoir bank landslide evolution during the water storage period.
As a continuation of mining subsidence, the surface secondary subsidence (including sinking and uplift) of closed mines poses a potential threat to the safety of surface buildings-structures in mining areas. However, at this stage, the law analysis of the surface secondary subsidence of closed mines under different geological mining conditions is not yet comprehensive. Due to the high underground water level, thick Quaternary loose layers, and multiple coal seams in the Huainan mining area, monitoring and analyzing the surface secondary subsidence of closed mines in the Huainan mining area has important theoretical and practical values.
First, in order to verify the reliability of the monitoring results, the StaMPS software is employed to simultaneously perform persistent scatterers interferometry and small baseline subset processing. Amplitude dispersion index and amplitude difference dispersion index are used to select coherent points, respectively. Then, the unwrapped phases of the coherent points are obtained by a three-dimensional phase unwrapping algorithm. Finally, the surface subsidence of the coherent points are obtained by using temporal-domain low-pass and spatial-domain high-pass filtering.
The results show that: (1) The surface uplifts after the closure of Xinjisan and Lizuizi-Xinzhuangzi-Xieyi mines, with a maximum uplift rate of 51.1 mm/a, and is located in Xinzhuangzi mine, while the surface of Panyi mine is still sinking due to its late closure in September 2018. (2) The surface secondary subsidence law of closed mines in Huainan mining area is sinking stage⁃stable stage⁃uplift stage, which is consistent with the surface subsidence law of closed mines in Xuzhou mining area, but the sinking, stable and uplift stages do not necessarily occur successively over time. (3) There may be a hydraulic connection between Lizuizi, Xinzhuangzi, and Xieyi mines. The groundwater first rises from the junction of Xinzhuangzi and Xieyi mines and then flows to the southeast and northwest sides.
Although the law of surface subsidence in the closed mines of Huainan and Xuzhou mines is relatively consistent, there are differences in the law of surface uplift due to different hydrogeological mining conditions. Therefore, in future work, we will continue to pay attention to the law of surface secondary subsidence of closed mines under different geological mining conditions.
The landslides disaster is one of the most important factors for the construction of major infrastructure in the western China. How to effectively and reliably carry out wide-area landslide susceptibility prediction has always been a frontier difficulty in domestic and foreign studies. However, the present data-driven methods for prediction of landslide susceptibility in large-scale and complex scenarios still face two major issues:(1) The difference between various landslide inducing environments in a wide range of scenarios, would cause the difficulty to applying a single model to account for multiple landslide phenomenon;(2) small sample problem: Complex environmental tasks require models with large capacity and strong representative power, but there is a lack of sufficient landslide samples in practice.
In response to the above problems, this paper takes Qijiang and Fuling District of Chongqing City, China as an example, proposes a local prediction strategy, and introduces the idea of meta-training an intermediate representation suited to be generalized, that can be adapted to the landslide sensitivity prediction task cor-responding to the current local area, with only a very small number of samples in the local area, and number of iterations. Thus, the two issues mentioned can be well settled.
The proposed method is different from traditional methods such as support vector machines, multilayer perceptrons, and random forests, which require a large number of samples and gradient iterations to train the supervised model. Instead, only a small sample is required to fine-tune the intermediate model, which still improves the global accuracy by 1%-5%, the precision by 1%-3%, the
The meta-learning paradigm enables few-shot adaptation of landslide susceptibility prediction model with superior statistical performance.
With the development of Shenzhen city, China, land renovation is more frequent. At the same time, affected by the subtropical monsoon climate, the area under the jurisdiction has abundant rainfall and dense vegetation coverage, making it difficult to identify the hidden dangers of geological hazards widely distributed on artificial slopes and natural slopes. Therefore, it is necessary to develop a set of hazard evaluation system of geological disaster that can solve the unique terrain and climate conditions in Shenzhen, so as to achieve the purpose of preventing disasters in advance and reducing casualties.
(1) On the basis of high-precision digital elevation model of Shenzhen city obtained by airborne light detection and ranging(LiDAR), about 3 500 slope disaster prone points in Shenzhen are obtained through data collection, remote sensing interpretation and field verification. The sample library expanded 330% after proofreading.(2) Taking 3 major factors (8 factors) of terrain, geological structure and human engineering activities into comprehensive consideration, and based on the rainfall-induced disaster mechanism, a rainfall collection factor is proposed, and the weight of evidence method is used to complete the geological disaster hazard evaluation model under rainfall-induced conditions.(3) The threshold determination method of“key point control”under the actual background of single disaster is proposed, and the classification of the risk assessment model is completed.
The area under curve value of receiver operating characteristic curve model reaches 0.903, indicating that the model has a good effect on disaster forecasting. LiDAR technology can improve the identification accuracy of geological hazards in cities under dense vegetation coverage.
Based on airborne LiDAR data, through a series of means such as expansion of disaster database, analysis of disaster distribution law, establishment of disaster evaluation factors, and classification of risk levels, it can form a refined evaluation system for the hazard evaluation of the slope in densely vegetated areas under the influence of the subtropical monsoon climate.
Because of high frequency of extreme weather, railway subgrade disaster shows a trend of increasing, great harmfulness and is hard to detect in advance. Even a small-size subgrade disaster may cause railway paralysis, which prevents the development of transportation. Many incidents illustrate that the earliest subgrade disaster always appeared in areas where no case ever reported before. It means that traditional monitoring methods are hard to detect hidden danger area. Thus, a promising method is urgent to be proposed for meeting the monitoring requirement for railway safety.
First, the characteristics of subgrade disaster are summarized under different disaster-pregnant environments, and the advantages of various monitoring methods are discussed to find a collaborative applications method for early identification of subgrade disaster. The potential hazards that may cause damage to subgrade structure and the deterioration degree of subgrade are considered as the main monitoring object of subgrade service status, including subgrade deformation monitoring, structural health monitoring, geological hazard monitoring along the railway, track irregularity, and external environmental monitoring. Second, an investigative approach based on the integration of space-air-train-ground muti-source techniques is proposed to detect the geohazards and monitor the service status of railway subgrade. It means that collaborative application of different monitoring methods, cooperative analysis of different scales and resolutions data and collaboration of between various railway departments are required. Two monitoring schemes for subgrade disasters and service status of subgrade are proposed based on the integration of multi-source and multi-scale monitoring technique. Finally, the development direction of subgrade service status monitoring is discussed.
This monitoring system has been applied in the identification of subgrade service status in the Shanghai-Nanjing high speed railway, which can quickly investigate the location of the disasters and the deterioration degree of subgrade.
On September 20, 2019, the Jungong ancient landslide in Lajia Town, Maqin County, Qinghai Province, China suffered a local failure, which led to the interruption of traffic and seriously threatened the safety of local residents' lives and properties. It is urgent to find out the deformation area and deformation law of the ancient landslide, and to provide the support for prevention and control enginee-ring design, monitoring and early warning.
First, using high-resolution satellite images, digital elevation model data and combined with field investigations, the ancient landslide were zoned based on landslide morphological characteristics and signs of deformations; further, Sentinel-1 radar satellite descending data from January 2017 to December 2020 were used to analyze the surface deformation characte-ristics and deformation patterns of the ancient landslide based on small baseline subset interferometric synthetic aperture radar technology (SBAS-InSAR).
Based on the morphological characteristics and deformation signs of the landslide, the ancient landslide was divided into four sub-areas. The SBAS-InSAR results show that the ancient landslide is in a continuous slow creeping state. The strong deformation area of the landslide is mainly located in the road excavation section. Human activities greatly disturb the stability of the ancient landslide. The deformation rate of the strong deformation area of the landslide has a good relationship with rainfall.
Although the ancient landslide has been partially treated with anti-slide piles and other projects, the ancient landslide has multi-level sliding surfaces, and the depth of the existing anti-slide piles is not enough. Although the ancient landslide has played a certain role in anti-slide, it has not completely prevented the creep deformation of the landslide. It is suggested that the subsequent treatment projects should use drilling and other exploration techniques to find out the depth of the multi-level sliding surfaces before designing, and installing on-site real-time monitoring and early warning equipment such as crack meters in the strongly deformed areas. Combined with the medium and long-term monitoring of radar satellite InSAR, a point-surface monitoring and early warning system could be realized.
The continuous exploitation of the Liaohe Delta oilfield has resulted in severe surface subsidence, impacting oil recovery rates, production operations, and posing threats to surface infrastructure and ecological environments. To ensure the safe exploitation of underground fluid resources and protect the regional environment, monitoring implementation is needed for this region.
The method of small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technology with coherence information as weights was used to analyze the surface deformation. Using the fusion decomposition of synthetic aperture radar descending and ascending orbit results to extract both vertical and horizontal east-west deformation within the Shuguang oilfield area. Subsequently, a reservoir compaction-induced subsidence inversion model is applied to the Shuguang oilfield to simulate and interpret the observed subsidence phenomena, linking them to the underground fluid resource exploitation activities.
The results reveal significant ground subsidence throughout the Liaohe Delta region, particularly in the Shuguang oilfield and Huanxiling oilfield. The average line of sight subsidence rates reaching 158 mm/a and 73 mm/a, respectively. In the Shuguang oilfield, there is horizontal movement towards the subsidence center, with approximately equal magnitudes of movement on the east and west sides. The maximum horizontal movement rate is observed to be -62 mm/a (westward motion). Furthermore, the reservoir compaction and subsidence model based on Shuguang oilfield reservoir parameters effectively invert the maximum subsidence position at the center of the oilfield, with subsidence range and magnitude consistent with the InSAR observation results.
The study concludes that continuous exploitation of oil has led to significant ground subsidence in the Liaohe Delta region, especially in Shuguang oilfield and Huanxiling oilfield, with clear patterns of subsidence and horizontal movement detected using SBAS-InSAR technology. The developed reservoir compaction-induced subsidence inversion model proves effective in simulating subsidence phenomena associated with oilfield operations. These findings underscore the importance of monitoring and managing subsidence risks to ensure the safe exploitation of underground resources and to protect regional ecological environments in the Liaohe Delta region.
Geological hazard points and hidden danger points are the data basis for geological hazard evaluation, while the existing records of geological hazard points have poor timeliness and are incomplete. To solve this problem, the deformation information obtained by multi-temporal interferometric synthetic aperture radar (InSAR) was integrated into the geological hazard evaluation model. And we explore how to make better use of the deformation information.
The greater the deformation level, the greater the possibility of geological hazards. This paper not only takes the deformation points obtained by multi-temporal InSAR as the geological hazard points/hidden danger points, but also integrates the deformation level information obtained by multi-temporal InSAR as an evaluation factor into the susceptibility evaluation model, making full use of the effective deformation information obtained by multi-temporal InSAR. And the evaluation model adopts the coupling model based on information value model and the analytic hierarchy process model to obtain the susceptibility evaluation and zoning of the geological hazards in Baiyin City, Gansu Province,China.
Through the verification of the existing geological disaster point data, it is found that the partitions in this paper are in good agreement with the existing geological hazard points distribution.In the designated extremely high-prone areas, there are nearly 8 geological disaster points within 10 km2, while less than one in the extremely low-prone areas.
The multi-temporal InSAR deformation information added to the geological hazard evaluation model greatly improves the timeliness and quantity of records of geological hazard points/hidden points. However, only one kind of synthetic aperture radar data cannot completely identify all geological hazard points/hidden danger points, due to the limitations of incidence angle and microwave wavelength. In the futher work, we will focus on the combination of multiple deformation monitoring technologies to jointly monitor surface deformation, such as multi-sensor and multi-track InSAR technology, airborne light laser detection and ranging and high-resolution optical remote sensing.
Flash floods are natural disasters caused by sudden rise in water levels in mountainous rivers, which are characterized by instantaneity and great destructiveness. In recent years, the frequent occurrence of flash floods in Longnan city, Gansu province, has posed a serious threat to the safety of local people's lives and property, thus it is urgent to carry out a risk assessment of flash floods in this region.
This study takes Longnan city as the study area, and utilizes the MaxEnt model combining with the particle swarm algorithm to evaluate the vulnerability of study area based on 834 flash flood hazard points investigated and 32 disaster-causing factors. It also predicts the spatial pattern changes and potential mass migration trends of the future flash flood vulnerability areas based on three periods of climate data from the current period (2021—2040) and the future period (2041—2060, 2061—2080, 2081—2100).
The area under receiver operating characteristic curve of the results of the study in each period is above 0.85, which indicates that the precision of the results of the method is good. The main cause factors in this study area are driest month precipitation, monthly mean diurnal temperature difference, coefficient of variation of precipitation, warmest month maximum temperature, land use, distance from the river, soil texture, profile curvature, elevation, and topographic relief. The flash flood-prone areas in the study area varies in different periods, but are mainly distributed in Wudu District, Wen County and Tanchang County, and the simulation results for the three future periods (2041—2060, 2061—2080, 2081—2100) reflected a decreasing trend compared with the current period (2021—2040).
Flood disaster is a very destructive natural disaster. The main reasons for its generation include heavy rainfall, storm surge and dam break. When flood disaster occurs in populated areas such as cities and towns, the flood will directly threaten the safety of life and property of residents. Also it will cause paralysis of land and underground transportation, interruption of water and electricity transportation. In the process of flood rescue, quick and accurate identification of flooded roads is conducive to planning appropriate personnel transfer and material transportation routes, and reducing subsequent losses caused by floods. Aiming at the problem that roads in the flood disaster scenario cannot be automatically and correctly identified, this paper proposes an end-to-end flooded road detection method based on deep learning.
The proposed method uses a three-branch encoder-decoder structure and uses strip convolution, in which the efficient extraction of linear features is realized. And the coordination dual attention mechanism can effectively guide the network, and realize the identification of road areas. The method can effectively utilize the historical optical remote sensing images before the disaster and the real-time optical remote sensing during the disaster. The image is applied to detect the flooded and non-flooded roads in the disaster-stricken area, and a comparative experiment is carried out on the self-built dataset.
The experimental results show that the precison and recall rate are 0.838 1 and 0.666 8 on pre-disaster road, 0.796 6 and 0.607 4 on post-disaster road, 0.780 0 and 0.661 4 on affected road respectively.
The proposed method has achieved the goal of automatically identifying the flooded roads in the flood-stricken area. The ability of detecting flooded roads and non-flooded roads can provide strong support for flood disaster rescue and reduce losses of life and property caused by flood disasters.
Convolutional neural network (CNN) was widely used in landslide susceptibility assessment because of its powerful feature extraction capability. However, with the demand for scene diversification and high accuracy, the algorithm of CNN was constantly improved. The practice of improving accuracy by deepening the network hierarchy or combining other models tended to increase the number of model parameters and the amount of calculation greatly, which led to the difficulty of model training or the overfitting of results, thus limiting its practical application. We want to solve the above problems from the model structure.
We proposed to build a multi-dimensional CNN coupling model. Through the asymmetric aggregation of feature maps, one-dimensional CNN(1D-CNN) and two-dimensional CNN(2D-CNN) were connected to maintain network depth, limit model parameters, and reduce computation. The parameters sharing of multi-dimensional convolution kernels was used to capture the deep coupling features of different dimensions and different landslide factors, so as to make full use of features and avoid overfitting. Taking Sedongpu gully in Xizang,China as the experimental area, this paper selected 11 kinds of landslide influencing factors to analyze the landslide susceptibility.
The results show that the multi-dimensional CNN coupling structure had the same training efficiency as the shallow 2D-CNN with fewer parameters due to the reduced computational effort. While compared with the deep 2D-CNN with the approximate number of parameters, the training time of the proposed method was significantly reduced and the training cost was lower. In addition, the coupled model had a stronger feature learning ability than the independent 1D-CNN and 2D-CNN, and therefore obtained higher model accuracy. Under each confusion matrix metric of the testing data, the coupled model received higher scores, and thus obtained more reliable landslide susceptibility assessment results.
The multi-dimensional CNN coupling model proposed in this paper is a reliable method applicable to landslide susceptibility assessment. This study provides new theoretical guidance and technical support for further landslide hazard monitoring and prevention.
The response law of ancient (old) landslides in the reservoir area is an important research topic. Previous research primarily analyzed real-time surface displacement and reservoir water level data. However, professional monitoring conditions are often lacking on most reservoir bank slopes. This complicates tracking the landslide's historical deformation. Satellite and airborne remote sensing platforms enable multi-scale, long-term monitoring of landslide deformation and damage.
This study employs multi-source three-dimensional observation technologies including aerial, space-based, and terrestrial platforms to monitor the deformation and evolution of the Pubugou Hydropower Station's Hongyanzi landslide over approximately 10 years. It utilizes unmanned aerial vehicle photography (optical imaging) and light detection and ranging (LiDAR) for detailed topographic mapping and deformation analysis from 2009 to 2020. Additionally, time-series interferometric synthetic aperture radar (InSAR) technology is used to track long-term surface deformations from October 2014 to July 2020. Field investigations have identified typical deformation and failure characteristics of the landslide, incorporating geological conditions and external factors such as rainfall and reservoir water levels to analyze causal mechanisms and dynamic trends.
The irregularly semicircular Hongyanzi landslide spans 20-50 m in thickness, encompasses approximately 15.53 million m³ in volume, and slides at an approximate bearing of 340°. Composed of quaternary pebbled stones, silty sand, and clay, the landslide's bed slopes between 20° and 25°. Its lithology includes Emeishan Formation basalt and Yangxin Formation dolomite. Existing since 2006 or earlier, the landslide features elements like walls and steps. LiDAR imagery from 2009 clearly delineates its boundaries, though it shows no new signs of deformation or failure. Following reservoir impoundment, the reactivated landslide develops new, widening cracks along its rear edge. Post-reactivation, the landslide predominantly undergos uniform deformation, with more significant movement at the trailing edge than the leading edge, without marked acceleration. Heavy rainfall is the most significant control factor, imparting stepwise deformation characteristics to the landslide.
A comprehensive analysis of multi-source data reveals that phenomena like the Hongyanzi landslide exhibit typical long-term, gradual, and seasonal movements. Long-term InSAR effectively captures these characteristics. Multi-stage optical remote sensing and surface point cloud data from LiDAR, after vegetation removal, enable more intuitive comparisons of macroscopic deformation across different landslide areas. Integrating this with geological assessments and field investigations allows for detailed engineering analyses to ascertain the causes, patterns, and future trends of landslide activity.
The urban lifeline safety project is a major national livelihood project, which ensures the safe operation of urban gas, water, electricity and other systems. Nowadays, the project is confronted with three types of difficulties: Difficulties in mechanism discovery and experimental reproduction; difficulties in risk detection and early identification, and difficulties in accurate early warning and collaborative prevention and control. In order to reveal the mechanism and reproduce the experiments, a full-size urban lifeline multi-hazard large-scale scientific device is firstly developed in the world, a series of dis-aster modes with 5 categories and 169 kinds of disasters coupled are established, based on which a comprehensive risk dynamic assessment method coupled with underground pipelines and above-ground disaster-bearing body is presented. To improve the risk detection and early identification, highly sensitive gas sensing laser chip as well as detector with active grating and wavelength-biased are invented. We develop an intelligent ball for leakage located in water supply pipe network based on the inertial guidance, force balance, acoustic spectrum and BeiDou. Bridge overall modal (fingerprint) monitoring technology is break-through. In order to solve the problem of accurate early warning and collaborative prevention and control, an urban lifeline safety risk physical heuristic artificial intelligence early warning technology is presented, based on which a monitoring and early warning system is developed with collaborative sensing, fusion monitoring, targeting early warning, linkage response and other functions. The related studies result in the development of the safety and emergency response industry, followed by the pioneering and substantial contributions to the prevention and control of urban lifeline risks.
In mid- to late- April 2024, an extreme heavy rainfall event occurred in Shaoguan City, Guangdong Province, inducing a large number of landslides in Jiangwan Town, Shaoguan. People lost connection with the outside world for nearly 36 hours, which aroused widespread social concern. Rapidly and accurately identifying the basic characteristics of landslides, development and distribution patterns and formation conditions is crucial for disaster emergency decision-making and risk elimination and disposal.
Using the post-disaster optical remote sensing images and combining with deep learning model, the rainfall-induced landslides in Jiangwan Town, Shaoguan, were quickly and automatically identified.
After manually calibration, a total of 1 192 landslides were deciphered, with a total area of about 3.14 km². The scale of the landslides was dominated by small and medium-sized landslides, which were mainly distributed as an aggregated belt along the river in the northeast-southwest direction, with a significant characteristic of concentrated occurrence. Spatial statistical analysis showed that the landslides were mainly distributed on concave slopes with slopes of 10°-30° in the range of 200-300 m elevation. Further quantitative analysis of the geomorphic controlling factors of landslides using the random forest model and SHAP theory reveals that different topographic and geomorphic factors have different degrees of nonlinear effects on landslide formation, and that multiple factors such as elevation, slope, and catchment conditions are coupled to jointly control the formation of landslides.
This paper highlights the great advantage of deep learning-based intelligent identification and analysis technology in the emergency investigation and formation conditions analysis of landslide disasters, which can provide important technical support for the rapid assessment of disaster losses and risk identification.
The construction of effective path set is the key link of traffic control and guidance of highway network, and plays an important role in post disaster path planning. Usually when a disaster occurs, there is a problem that the current emergency route planning does not incorporate the dynamic changes of disasters in time, which affects the subsequent emergency evacuation and rescue and relief.
To solve this problem, a double-layer road network model is proposed, which couples the topological structure of the highway network with the traffic flow and the state of disasters and events. The linear reference and dynamic segmentation technology is introduced to associate the routes and events and the depth first search algorithm is improved based on Dijkstra algorithm.
(1) Double-layer road network model reflects the topological relationship between road sections, stores the dynamic attribute information of road sections and realizes the search and construction of effective path sets. (2) Improved DFS algorithm reduces the computational complexity and proposes an effective path search algorithm combining with time-varying road network, disasters and traffic conditions.
The example application and verification in the study area shows that the method can dynamically search the effective path set according to time-varying traffic conditions and disasters. This model enhances the expression and analysis ability of road data, and is able to serve traffic analysis and control under the change of disaster situation, and is suitable for the post disaster traffic operation situation assessment of road network.
Studying the spatiotemporal changes of groundwater storage(GWS) and its sustainable spatiotemporal evolution characteristics in the upper reaches of the Yellow River Basin can provide a valuable reference for the sustainable and rational development of water resources in the Yellow River Basin.
The changes of terrestrial water storage (TWS) and GWS in the upper reaches of the Yellow River Basin from April 2002 to December 2022 were estimated using Masson data and spherical harmonic (SH) data from GRACE (gravity recovery and climate experience) and GRACE-FO (GRACE follow on) gravity satellites, combined with prior hydrological models, and their temporal and spatial variation characteristics were also analyzed. The sustainability index (SI) of water resources in the study area was further calculated to evaluate the sustainability of regional groundwater. And the correlation and contribution of precipitation change, normalized difference vegetation index (NDVI), evapotranspiration (ET) and regional GWS were discussed.
The change of GWS in the study area showed an overall downward trend at a rate of approximately -3.89 ± 0.37 mm / a, and showed obvious spatial characteristics of the difference between the southern and northern values, which was consistent with the monitoring results of the measured wells (correlation was approximately 0.73). During the study period, the regional groundwater was almost in a state of severe unsustainability, and the spatial sustainability also gradually decreased from south to north, with an average sustainability index of only 0.38. The size of the contribution measure shows that NDVI has the greatest impact on the change of GWS in the study area, ET has the second highest impact,and the rainfall has the smallest; there was a significant negative correlation between NDVI, ET and regional GWS changes (correlation coefficients were approximately -0.76 and -0.77, respectively). The rainfall in the south and north of the study area was positively correlated with the corresponding GWS changes (correlation coefficients were approximately 0.54 and 0.50, respectively).
This study effectively evaluated the changes of TWS and GWS in the upper reaches of the Yellow River Basin over the past 20 years, and reasonably evaluated the spatiotemporal distribution characteristics of groundwater sustainability in the upper reaches of the Yellow River, as well as the correlation between external influences and GWS.
In order to improve the efficiency of landslide extraction and explore the spatial-temporal distribution characteristics of regional landslides and single landslide, a rapid landslide extraction model is designed to provide a scientific basis for landslide disaster prevention and management.
Based on multi-temporal domestic high-resolution remote sensing satellite images, advanced land observing satellite 12.5 m digital elevation model, and historical landslide hidden danger points, this study selected 8 landslide-prone townships located in the northwest of Fengjie County, Chongqing city,China to form the study area. Feature optimization based on SHAP in terpretation framework and Bayesian hyperparameter automatic optimization based on Optuna framework are introduced into XGBoost algorithm to construct and optimize the landslide extraction model. The study realized rapid extraction of landslide spatial information and quantitative analysis of landslide spatialtemporal distribution in 2013, 2015, 2018 and 2020.
In the comparison of models accuracy, precision, Kappa coefficient and area under curve value of landslide extraction model constructed by optimized XGBoost basic algorithm are 96.26%, 90.91%, 0.860 2 and 0.970 5, respectively, which higher than GBDT, LightGBM and Adaboost.
From 2013 to 2020, the overall development degree of landslides in the study area is relatively high, and the spatial distribution of landslides is uneven among villages and towns, and the landslides is distributed on both sides of the river valley and the river, showing the characteristics of regional concentration. From 2013 to 2020, Miaowan landslide has high development intensity, strong activity, repeated resurrection phenomenon and induced new landslides. The slope of the landslide is about 25°-45°, and it’s topographic characteristics change little, significant changes mainly focus on color, texture, geometry and vegetation coverage.
Pixel offset tracking (POT) for optical remote sensing imagery is widely used to invert coseismic deformation fields and monitor landslides. Traditional pixel offset tracking method estimates the displacement of the central pixel by searching for the matching window with the highest correlation, which is computationally inefficient and suffers from inaccurate deformation boundary extraction due to the decoherence effects in the region with dynamical deformation. We introduce the optical flow field model commonly used in computer vision to the pixel offset tracking problem to obtain accurate surface deformation efficiently.
The optical flow field method applicable to optical remote sensing images and the improved inversion algorithm for the time series analysis are proposed to inverse the surface deformation. Experiments on the simulated coseismic deformation fields in Tajikistan are detailed to assess the feasibility and the minimum detectable deformation of the optical flow field method. The advantages of the proposed method over computational cost and deformation boundary extraction accuracy are illustrated by the co-seismic deformation field of the California earthquake and the displacement of the Baige landslide. Furthermore, the performance on estimating large gradient deformation and the robustness of the improved time series inversion algorithm are discussed by analyzing the time series deformation of the Baige landslide.
The results show that compared with the traditional window correlation matching method, the optical flow field method has an offset tracking accuracy of 0.032 pixel, which improves the computational efficiency by about 20 times, and the accuracy of the deformation zone is improved by 25.9%. The time series weighted inversion algorithm reduces the uncertainties in the estimation of east-west and north-south displacements of optical remote sensing images by 16.2% and 12.4%, respectively.
The proposed method alleviates the pixel offset tracking problem in the boundary region with large gradient deformation.
Mine slope instability is one of the main factors restricting the safety production of open-pit mines in China. Ground-based synthetic aperture radar interferometry technology has been gradually introduced into the application of slope safety monitoring and early warning prediction in open-pit mines. However, the high-frequency rolling update characteristics of ground radar interferometry data lead to large data error accumulation and unobvious curve mutation characteristics.
Processing the original data by dislocation subtraction and velocity reciprocal method can effectively reduce the vibration of high-frequency data, improve the readability of critical sliding data. After data processing, it can highlight the trend characteristics of key deformation data. The research is based on the analysis of cumulative displacement curve, velocity curve and reciprocal velocity curve group treated with different periods.
It is found that there are three characteristic points in the curve group: Sudden deformation increase point, velocity increase point and stable vibration point. Through these characteristic points, the slope landslide disaster can be predicted. The trend of key deformation data can be highlighted by using the three feature points of deformation sudden increase point, velocity growth point and stable vibration point.
Through the identification of three feature points, the possible landslide can be effectively identified in advance and the landslide time can be predicted, which provides a new technical path and solution for landslide early warning and prediction analysis based on ground-based interferometric radar.
Land subsidence caused by long-term over-exploitation of groundwater is one of major problems in Beijing. Since the opening of the South-to-North Water Transfer Project, the problem of water shortage in Beijing has been greatly alleviated, and the pressure of land subsidence has been reduced to a certain extent.
In order to analyze the development of land subsidence after the start of the South-to-North Water Transfer in Beijing, ascending and descending time-series interferometric synthetic aperture radar (InSAR) technique is used to monitor land subsidence in Beijing. First, the mean deformation velocity and cumulative deformation in line of sight in Beijing from January 2015 to December 2020 is obtained by the small baseline subset InSAR. Second, the robust least square fitting method is used to fuse the deformation results of the lifting rail, after that the global positioning system monitoring data are compared with the fusion results of lifting rail. Finally, the variation trend between the deformation results obtained by the robust least quadratic fitting and groundwater data is analyzed.
The deformation results show that the center of Beijing is basically stable and the deformation distribution is not uniform. The maximum ascending annual deformation velocity and the maximum ascending cumulative deformation amount reach -134 mm/a and -697 mm respectively. The maximum descending annual deformation velocity and the maximum descending cumulative deformation amount reach -135 mm/a and -734 mm respectively. And the fusion results obtained by the least square fitting method has reliability and accuracy.
The subsidence rate in Beijing shows a decreasing trend with the gradual increase of groundwater level. In general, the middle route of South-to-North Water Transfer Project has alleviated the expansion trend of land subsidence in Beijing to a certain extent.
In the research work of reservoir landslide displacement prediction, due to the lag of reservoir water level response, it is difficult for the traditional landslide displacement prediction model to analyze the monotonically increasing step deformation characteristics, which seriously affects the prediction results, and it is necessary to establish a landslide displacement prediction model that can consider the time lag effect.
We analyze the time lag effect of reservoir levels separately through grey correlation, account for the cumulative effect of earlier rainfall, and consider the effect of earthquake on landslide deformation, and establishe an autoregressive distributed lag landslide displacement prediction model that can be applied to engineering sites.
The results show that: (1) The engineering case study concluded that rising reservoir levels and earthquakes were the main triggering factors for the increased deformation of the landslide, and the lag time of reservoir levels acting on landslide deformation was 8 days. (2) The correlation coefficient between the cumulative landslide displacement and the actual displacement calculated by the new model is as high as 0.992 7, with a root mean square error of 14.11 mm. (3) The calculation of trend speed ratio indicators can provide a new sensitivity evaluation parameter for landslide monitoring and early warning.
The study establishes a physically significant prediction model for reservoir bank landslide displacements, provides a comprehensive analysis of landslide displacements, achieves a quantitative calculation of the seismic contribution to landslide displacement evolution, and provides new technical support for the safety risk management of the whole process of reservoir bank landslide evolution during the water storage period.
As a continuation of mining subsidence, the surface secondary subsidence (including sinking and uplift) of closed mines poses a potential threat to the safety of surface buildings-structures in mining areas. However, at this stage, the law analysis of the surface secondary subsidence of closed mines under different geological mining conditions is not yet comprehensive. Due to the high underground water level, thick Quaternary loose layers, and multiple coal seams in the Huainan mining area, monitoring and analyzing the surface secondary subsidence of closed mines in the Huainan mining area has important theoretical and practical values.
First, in order to verify the reliability of the monitoring results, the StaMPS software is employed to simultaneously perform persistent scatterers interferometry and small baseline subset processing. Amplitude dispersion index and amplitude difference dispersion index are used to select coherent points, respectively. Then, the unwrapped phases of the coherent points are obtained by a three-dimensional phase unwrapping algorithm. Finally, the surface subsidence of the coherent points are obtained by using temporal-domain low-pass and spatial-domain high-pass filtering.
The results show that: (1) The surface uplifts after the closure of Xinjisan and Lizuizi-Xinzhuangzi-Xieyi mines, with a maximum uplift rate of 51.1 mm/a, and is located in Xinzhuangzi mine, while the surface of Panyi mine is still sinking due to its late closure in September 2018. (2) The surface secondary subsidence law of closed mines in Huainan mining area is sinking stage⁃stable stage⁃uplift stage, which is consistent with the surface subsidence law of closed mines in Xuzhou mining area, but the sinking, stable and uplift stages do not necessarily occur successively over time. (3) There may be a hydraulic connection between Lizuizi, Xinzhuangzi, and Xieyi mines. The groundwater first rises from the junction of Xinzhuangzi and Xieyi mines and then flows to the southeast and northwest sides.
Although the law of surface subsidence in the closed mines of Huainan and Xuzhou mines is relatively consistent, there are differences in the law of surface uplift due to different hydrogeological mining conditions. Therefore, in future work, we will continue to pay attention to the law of surface secondary subsidence of closed mines under different geological mining conditions.
The landslides disaster is one of the most important factors for the construction of major infrastructure in the western China. How to effectively and reliably carry out wide-area landslide susceptibility prediction has always been a frontier difficulty in domestic and foreign studies. However, the present data-driven methods for prediction of landslide susceptibility in large-scale and complex scenarios still face two major issues:(1) The difference between various landslide inducing environments in a wide range of scenarios, would cause the difficulty to applying a single model to account for multiple landslide phenomenon;(2) small sample problem: Complex environmental tasks require models with large capacity and strong representative power, but there is a lack of sufficient landslide samples in practice.
In response to the above problems, this paper takes Qijiang and Fuling District of Chongqing City, China as an example, proposes a local prediction strategy, and introduces the idea of meta-training an intermediate representation suited to be generalized, that can be adapted to the landslide sensitivity prediction task cor-responding to the current local area, with only a very small number of samples in the local area, and number of iterations. Thus, the two issues mentioned can be well settled.
The proposed method is different from traditional methods such as support vector machines, multilayer perceptrons, and random forests, which require a large number of samples and gradient iterations to train the supervised model. Instead, only a small sample is required to fine-tune the intermediate model, which still improves the global accuracy by 1%-5%, the precision by 1%-3%, the
The meta-learning paradigm enables few-shot adaptation of landslide susceptibility prediction model with superior statistical performance.
With the development of Shenzhen city, China, land renovation is more frequent. At the same time, affected by the subtropical monsoon climate, the area under the jurisdiction has abundant rainfall and dense vegetation coverage, making it difficult to identify the hidden dangers of geological hazards widely distributed on artificial slopes and natural slopes. Therefore, it is necessary to develop a set of hazard evaluation system of geological disaster that can solve the unique terrain and climate conditions in Shenzhen, so as to achieve the purpose of preventing disasters in advance and reducing casualties.
(1) On the basis of high-precision digital elevation model of Shenzhen city obtained by airborne light detection and ranging(LiDAR), about 3 500 slope disaster prone points in Shenzhen are obtained through data collection, remote sensing interpretation and field verification. The sample library expanded 330% after proofreading.(2) Taking 3 major factors (8 factors) of terrain, geological structure and human engineering activities into comprehensive consideration, and based on the rainfall-induced disaster mechanism, a rainfall collection factor is proposed, and the weight of evidence method is used to complete the geological disaster hazard evaluation model under rainfall-induced conditions.(3) The threshold determination method of“key point control”under the actual background of single disaster is proposed, and the classification of the risk assessment model is completed.
The area under curve value of receiver operating characteristic curve model reaches 0.903, indicating that the model has a good effect on disaster forecasting. LiDAR technology can improve the identification accuracy of geological hazards in cities under dense vegetation coverage.
Based on airborne LiDAR data, through a series of means such as expansion of disaster database, analysis of disaster distribution law, establishment of disaster evaluation factors, and classification of risk levels, it can form a refined evaluation system for the hazard evaluation of the slope in densely vegetated areas under the influence of the subtropical monsoon climate.
Because of high frequency of extreme weather, railway subgrade disaster shows a trend of increasing, great harmfulness and is hard to detect in advance. Even a small-size subgrade disaster may cause railway paralysis, which prevents the development of transportation. Many incidents illustrate that the earliest subgrade disaster always appeared in areas where no case ever reported before. It means that traditional monitoring methods are hard to detect hidden danger area. Thus, a promising method is urgent to be proposed for meeting the monitoring requirement for railway safety.
First, the characteristics of subgrade disaster are summarized under different disaster-pregnant environments, and the advantages of various monitoring methods are discussed to find a collaborative applications method for early identification of subgrade disaster. The potential hazards that may cause damage to subgrade structure and the deterioration degree of subgrade are considered as the main monitoring object of subgrade service status, including subgrade deformation monitoring, structural health monitoring, geological hazard monitoring along the railway, track irregularity, and external environmental monitoring. Second, an investigative approach based on the integration of space-air-train-ground muti-source techniques is proposed to detect the geohazards and monitor the service status of railway subgrade. It means that collaborative application of different monitoring methods, cooperative analysis of different scales and resolutions data and collaboration of between various railway departments are required. Two monitoring schemes for subgrade disasters and service status of subgrade are proposed based on the integration of multi-source and multi-scale monitoring technique. Finally, the development direction of subgrade service status monitoring is discussed.
This monitoring system has been applied in the identification of subgrade service status in the Shanghai-Nanjing high speed railway, which can quickly investigate the location of the disasters and the deterioration degree of subgrade.
On September 20, 2019, the Jungong ancient landslide in Lajia Town, Maqin County, Qinghai Province, China suffered a local failure, which led to the interruption of traffic and seriously threatened the safety of local residents' lives and properties. It is urgent to find out the deformation area and deformation law of the ancient landslide, and to provide the support for prevention and control enginee-ring design, monitoring and early warning.
First, using high-resolution satellite images, digital elevation model data and combined with field investigations, the ancient landslide were zoned based on landslide morphological characteristics and signs of deformations; further, Sentinel-1 radar satellite descending data from January 2017 to December 2020 were used to analyze the surface deformation characte-ristics and deformation patterns of the ancient landslide based on small baseline subset interferometric synthetic aperture radar technology (SBAS-InSAR).
Based on the morphological characteristics and deformation signs of the landslide, the ancient landslide was divided into four sub-areas. The SBAS-InSAR results show that the ancient landslide is in a continuous slow creeping state. The strong deformation area of the landslide is mainly located in the road excavation section. Human activities greatly disturb the stability of the ancient landslide. The deformation rate of the strong deformation area of the landslide has a good relationship with rainfall.
Although the ancient landslide has been partially treated with anti-slide piles and other projects, the ancient landslide has multi-level sliding surfaces, and the depth of the existing anti-slide piles is not enough. Although the ancient landslide has played a certain role in anti-slide, it has not completely prevented the creep deformation of the landslide. It is suggested that the subsequent treatment projects should use drilling and other exploration techniques to find out the depth of the multi-level sliding surfaces before designing, and installing on-site real-time monitoring and early warning equipment such as crack meters in the strongly deformed areas. Combined with the medium and long-term monitoring of radar satellite InSAR, a point-surface monitoring and early warning system could be realized.
The continuous exploitation of the Liaohe Delta oilfield has resulted in severe surface subsidence, impacting oil recovery rates, production operations, and posing threats to surface infrastructure and ecological environments. To ensure the safe exploitation of underground fluid resources and protect the regional environment, monitoring implementation is needed for this region.
The method of small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technology with coherence information as weights was used to analyze the surface deformation. Using the fusion decomposition of synthetic aperture radar descending and ascending orbit results to extract both vertical and horizontal east-west deformation within the Shuguang oilfield area. Subsequently, a reservoir compaction-induced subsidence inversion model is applied to the Shuguang oilfield to simulate and interpret the observed subsidence phenomena, linking them to the underground fluid resource exploitation activities.
The results reveal significant ground subsidence throughout the Liaohe Delta region, particularly in the Shuguang oilfield and Huanxiling oilfield. The average line of sight subsidence rates reaching 158 mm/a and 73 mm/a, respectively. In the Shuguang oilfield, there is horizontal movement towards the subsidence center, with approximately equal magnitudes of movement on the east and west sides. The maximum horizontal movement rate is observed to be -62 mm/a (westward motion). Furthermore, the reservoir compaction and subsidence model based on Shuguang oilfield reservoir parameters effectively invert the maximum subsidence position at the center of the oilfield, with subsidence range and magnitude consistent with the InSAR observation results.
The study concludes that continuous exploitation of oil has led to significant ground subsidence in the Liaohe Delta region, especially in Shuguang oilfield and Huanxiling oilfield, with clear patterns of subsidence and horizontal movement detected using SBAS-InSAR technology. The developed reservoir compaction-induced subsidence inversion model proves effective in simulating subsidence phenomena associated with oilfield operations. These findings underscore the importance of monitoring and managing subsidence risks to ensure the safe exploitation of underground resources and to protect regional ecological environments in the Liaohe Delta region.
Geological hazard points and hidden danger points are the data basis for geological hazard evaluation, while the existing records of geological hazard points have poor timeliness and are incomplete. To solve this problem, the deformation information obtained by multi-temporal interferometric synthetic aperture radar (InSAR) was integrated into the geological hazard evaluation model. And we explore how to make better use of the deformation information.
The greater the deformation level, the greater the possibility of geological hazards. This paper not only takes the deformation points obtained by multi-temporal InSAR as the geological hazard points/hidden danger points, but also integrates the deformation level information obtained by multi-temporal InSAR as an evaluation factor into the susceptibility evaluation model, making full use of the effective deformation information obtained by multi-temporal InSAR. And the evaluation model adopts the coupling model based on information value model and the analytic hierarchy process model to obtain the susceptibility evaluation and zoning of the geological hazards in Baiyin City, Gansu Province,China.
Through the verification of the existing geological disaster point data, it is found that the partitions in this paper are in good agreement with the existing geological hazard points distribution.In the designated extremely high-prone areas, there are nearly 8 geological disaster points within 10 km2, while less than one in the extremely low-prone areas.
The multi-temporal InSAR deformation information added to the geological hazard evaluation model greatly improves the timeliness and quantity of records of geological hazard points/hidden points. However, only one kind of synthetic aperture radar data cannot completely identify all geological hazard points/hidden danger points, due to the limitations of incidence angle and microwave wavelength. In the futher work, we will focus on the combination of multiple deformation monitoring technologies to jointly monitor surface deformation, such as multi-sensor and multi-track InSAR technology, airborne light laser detection and ranging and high-resolution optical remote sensing.
Flash floods are natural disasters caused by sudden rise in water levels in mountainous rivers, which are characterized by instantaneity and great destructiveness. In recent years, the frequent occurrence of flash floods in Longnan city, Gansu province, has posed a serious threat to the safety of local people's lives and property, thus it is urgent to carry out a risk assessment of flash floods in this region.
This study takes Longnan city as the study area, and utilizes the MaxEnt model combining with the particle swarm algorithm to evaluate the vulnerability of study area based on 834 flash flood hazard points investigated and 32 disaster-causing factors. It also predicts the spatial pattern changes and potential mass migration trends of the future flash flood vulnerability areas based on three periods of climate data from the current period (2021—2040) and the future period (2041—2060, 2061—2080, 2081—2100).
The area under receiver operating characteristic curve of the results of the study in each period is above 0.85, which indicates that the precision of the results of the method is good. The main cause factors in this study area are driest month precipitation, monthly mean diurnal temperature difference, coefficient of variation of precipitation, warmest month maximum temperature, land use, distance from the river, soil texture, profile curvature, elevation, and topographic relief. The flash flood-prone areas in the study area varies in different periods, but are mainly distributed in Wudu District, Wen County and Tanchang County, and the simulation results for the three future periods (2041—2060, 2061—2080, 2081—2100) reflected a decreasing trend compared with the current period (2021—2040).
Flood disaster is a very destructive natural disaster. The main reasons for its generation include heavy rainfall, storm surge and dam break. When flood disaster occurs in populated areas such as cities and towns, the flood will directly threaten the safety of life and property of residents. Also it will cause paralysis of land and underground transportation, interruption of water and electricity transportation. In the process of flood rescue, quick and accurate identification of flooded roads is conducive to planning appropriate personnel transfer and material transportation routes, and reducing subsequent losses caused by floods. Aiming at the problem that roads in the flood disaster scenario cannot be automatically and correctly identified, this paper proposes an end-to-end flooded road detection method based on deep learning.
The proposed method uses a three-branch encoder-decoder structure and uses strip convolution, in which the efficient extraction of linear features is realized. And the coordination dual attention mechanism can effectively guide the network, and realize the identification of road areas. The method can effectively utilize the historical optical remote sensing images before the disaster and the real-time optical remote sensing during the disaster. The image is applied to detect the flooded and non-flooded roads in the disaster-stricken area, and a comparative experiment is carried out on the self-built dataset.
The experimental results show that the precison and recall rate are 0.838 1 and 0.666 8 on pre-disaster road, 0.796 6 and 0.607 4 on post-disaster road, 0.780 0 and 0.661 4 on affected road respectively.
The proposed method has achieved the goal of automatically identifying the flooded roads in the flood-stricken area. The ability of detecting flooded roads and non-flooded roads can provide strong support for flood disaster rescue and reduce losses of life and property caused by flood disasters.
Convolutional neural network (CNN) was widely used in landslide susceptibility assessment because of its powerful feature extraction capability. However, with the demand for scene diversification and high accuracy, the algorithm of CNN was constantly improved. The practice of improving accuracy by deepening the network hierarchy or combining other models tended to increase the number of model parameters and the amount of calculation greatly, which led to the difficulty of model training or the overfitting of results, thus limiting its practical application. We want to solve the above problems from the model structure.
We proposed to build a multi-dimensional CNN coupling model. Through the asymmetric aggregation of feature maps, one-dimensional CNN(1D-CNN) and two-dimensional CNN(2D-CNN) were connected to maintain network depth, limit model parameters, and reduce computation. The parameters sharing of multi-dimensional convolution kernels was used to capture the deep coupling features of different dimensions and different landslide factors, so as to make full use of features and avoid overfitting. Taking Sedongpu gully in Xizang,China as the experimental area, this paper selected 11 kinds of landslide influencing factors to analyze the landslide susceptibility.
The results show that the multi-dimensional CNN coupling structure had the same training efficiency as the shallow 2D-CNN with fewer parameters due to the reduced computational effort. While compared with the deep 2D-CNN with the approximate number of parameters, the training time of the proposed method was significantly reduced and the training cost was lower. In addition, the coupled model had a stronger feature learning ability than the independent 1D-CNN and 2D-CNN, and therefore obtained higher model accuracy. Under each confusion matrix metric of the testing data, the coupled model received higher scores, and thus obtained more reliable landslide susceptibility assessment results.
The multi-dimensional CNN coupling model proposed in this paper is a reliable method applicable to landslide susceptibility assessment. This study provides new theoretical guidance and technical support for further landslide hazard monitoring and prevention.
The response law of ancient (old) landslides in the reservoir area is an important research topic. Previous research primarily analyzed real-time surface displacement and reservoir water level data. However, professional monitoring conditions are often lacking on most reservoir bank slopes. This complicates tracking the landslide's historical deformation. Satellite and airborne remote sensing platforms enable multi-scale, long-term monitoring of landslide deformation and damage.
This study employs multi-source three-dimensional observation technologies including aerial, space-based, and terrestrial platforms to monitor the deformation and evolution of the Pubugou Hydropower Station's Hongyanzi landslide over approximately 10 years. It utilizes unmanned aerial vehicle photography (optical imaging) and light detection and ranging (LiDAR) for detailed topographic mapping and deformation analysis from 2009 to 2020. Additionally, time-series interferometric synthetic aperture radar (InSAR) technology is used to track long-term surface deformations from October 2014 to July 2020. Field investigations have identified typical deformation and failure characteristics of the landslide, incorporating geological conditions and external factors such as rainfall and reservoir water levels to analyze causal mechanisms and dynamic trends.
The irregularly semicircular Hongyanzi landslide spans 20-50 m in thickness, encompasses approximately 15.53 million m³ in volume, and slides at an approximate bearing of 340°. Composed of quaternary pebbled stones, silty sand, and clay, the landslide's bed slopes between 20° and 25°. Its lithology includes Emeishan Formation basalt and Yangxin Formation dolomite. Existing since 2006 or earlier, the landslide features elements like walls and steps. LiDAR imagery from 2009 clearly delineates its boundaries, though it shows no new signs of deformation or failure. Following reservoir impoundment, the reactivated landslide develops new, widening cracks along its rear edge. Post-reactivation, the landslide predominantly undergos uniform deformation, with more significant movement at the trailing edge than the leading edge, without marked acceleration. Heavy rainfall is the most significant control factor, imparting stepwise deformation characteristics to the landslide.
A comprehensive analysis of multi-source data reveals that phenomena like the Hongyanzi landslide exhibit typical long-term, gradual, and seasonal movements. Long-term InSAR effectively captures these characteristics. Multi-stage optical remote sensing and surface point cloud data from LiDAR, after vegetation removal, enable more intuitive comparisons of macroscopic deformation across different landslide areas. Integrating this with geological assessments and field investigations allows for detailed engineering analyses to ascertain the causes, patterns, and future trends of landslide activity.
The urban lifeline safety project is a major national livelihood project, which ensures the safe operation of urban gas, water, electricity and other systems. Nowadays, the project is confronted with three types of difficulties: Difficulties in mechanism discovery and experimental reproduction; difficulties in risk detection and early identification, and difficulties in accurate early warning and collaborative prevention and control. In order to reveal the mechanism and reproduce the experiments, a full-size urban lifeline multi-hazard large-scale scientific device is firstly developed in the world, a series of dis-aster modes with 5 categories and 169 kinds of disasters coupled are established, based on which a comprehensive risk dynamic assessment method coupled with underground pipelines and above-ground disaster-bearing body is presented. To improve the risk detection and early identification, highly sensitive gas sensing laser chip as well as detector with active grating and wavelength-biased are invented. We develop an intelligent ball for leakage located in water supply pipe network based on the inertial guidance, force balance, acoustic spectrum and BeiDou. Bridge overall modal (fingerprint) monitoring technology is break-through. In order to solve the problem of accurate early warning and collaborative prevention and control, an urban lifeline safety risk physical heuristic artificial intelligence early warning technology is presented, based on which a monitoring and early warning system is developed with collaborative sensing, fusion monitoring, targeting early warning, linkage response and other functions. The related studies result in the development of the safety and emergency response industry, followed by the pioneering and substantial contributions to the prevention and control of urban lifeline risks.
In mid- to late- April 2024, an extreme heavy rainfall event occurred in Shaoguan City, Guangdong Province, inducing a large number of landslides in Jiangwan Town, Shaoguan. People lost connection with the outside world for nearly 36 hours, which aroused widespread social concern. Rapidly and accurately identifying the basic characteristics of landslides, development and distribution patterns and formation conditions is crucial for disaster emergency decision-making and risk elimination and disposal.
Using the post-disaster optical remote sensing images and combining with deep learning model, the rainfall-induced landslides in Jiangwan Town, Shaoguan, were quickly and automatically identified.
After manually calibration, a total of 1 192 landslides were deciphered, with a total area of about 3.14 km². The scale of the landslides was dominated by small and medium-sized landslides, which were mainly distributed as an aggregated belt along the river in the northeast-southwest direction, with a significant characteristic of concentrated occurrence. Spatial statistical analysis showed that the landslides were mainly distributed on concave slopes with slopes of 10°-30° in the range of 200-300 m elevation. Further quantitative analysis of the geomorphic controlling factors of landslides using the random forest model and SHAP theory reveals that different topographic and geomorphic factors have different degrees of nonlinear effects on landslide formation, and that multiple factors such as elevation, slope, and catchment conditions are coupled to jointly control the formation of landslides.
This paper highlights the great advantage of deep learning-based intelligent identification and analysis technology in the emergency investigation and formation conditions analysis of landslide disasters, which can provide important technical support for the rapid assessment of disaster losses and risk identification.
The construction of effective path set is the key link of traffic control and guidance of highway network, and plays an important role in post disaster path planning. Usually when a disaster occurs, there is a problem that the current emergency route planning does not incorporate the dynamic changes of disasters in time, which affects the subsequent emergency evacuation and rescue and relief.
To solve this problem, a double-layer road network model is proposed, which couples the topological structure of the highway network with the traffic flow and the state of disasters and events. The linear reference and dynamic segmentation technology is introduced to associate the routes and events and the depth first search algorithm is improved based on Dijkstra algorithm.
(1) Double-layer road network model reflects the topological relationship between road sections, stores the dynamic attribute information of road sections and realizes the search and construction of effective path sets. (2) Improved DFS algorithm reduces the computational complexity and proposes an effective path search algorithm combining with time-varying road network, disasters and traffic conditions.
The example application and verification in the study area shows that the method can dynamically search the effective path set according to time-varying traffic conditions and disasters. This model enhances the expression and analysis ability of road data, and is able to serve traffic analysis and control under the change of disaster situation, and is suitable for the post disaster traffic operation situation assessment of road network.
Studying the spatiotemporal changes of groundwater storage(GWS) and its sustainable spatiotemporal evolution characteristics in the upper reaches of the Yellow River Basin can provide a valuable reference for the sustainable and rational development of water resources in the Yellow River Basin.
The changes of terrestrial water storage (TWS) and GWS in the upper reaches of the Yellow River Basin from April 2002 to December 2022 were estimated using Masson data and spherical harmonic (SH) data from GRACE (gravity recovery and climate experience) and GRACE-FO (GRACE follow on) gravity satellites, combined with prior hydrological models, and their temporal and spatial variation characteristics were also analyzed. The sustainability index (SI) of water resources in the study area was further calculated to evaluate the sustainability of regional groundwater. And the correlation and contribution of precipitation change, normalized difference vegetation index (NDVI), evapotranspiration (ET) and regional GWS were discussed.
The change of GWS in the study area showed an overall downward trend at a rate of approximately -3.89 ± 0.37 mm / a, and showed obvious spatial characteristics of the difference between the southern and northern values, which was consistent with the monitoring results of the measured wells (correlation was approximately 0.73). During the study period, the regional groundwater was almost in a state of severe unsustainability, and the spatial sustainability also gradually decreased from south to north, with an average sustainability index of only 0.38. The size of the contribution measure shows that NDVI has the greatest impact on the change of GWS in the study area, ET has the second highest impact,and the rainfall has the smallest; there was a significant negative correlation between NDVI, ET and regional GWS changes (correlation coefficients were approximately -0.76 and -0.77, respectively). The rainfall in the south and north of the study area was positively correlated with the corresponding GWS changes (correlation coefficients were approximately 0.54 and 0.50, respectively).
This study effectively evaluated the changes of TWS and GWS in the upper reaches of the Yellow River Basin over the past 20 years, and reasonably evaluated the spatiotemporal distribution characteristics of groundwater sustainability in the upper reaches of the Yellow River, as well as the correlation between external influences and GWS.
In order to improve the efficiency of landslide extraction and explore the spatial-temporal distribution characteristics of regional landslides and single landslide, a rapid landslide extraction model is designed to provide a scientific basis for landslide disaster prevention and management.
Based on multi-temporal domestic high-resolution remote sensing satellite images, advanced land observing satellite 12.5 m digital elevation model, and historical landslide hidden danger points, this study selected 8 landslide-prone townships located in the northwest of Fengjie County, Chongqing city,China to form the study area. Feature optimization based on SHAP in terpretation framework and Bayesian hyperparameter automatic optimization based on Optuna framework are introduced into XGBoost algorithm to construct and optimize the landslide extraction model. The study realized rapid extraction of landslide spatial information and quantitative analysis of landslide spatialtemporal distribution in 2013, 2015, 2018 and 2020.
In the comparison of models accuracy, precision, Kappa coefficient and area under curve value of landslide extraction model constructed by optimized XGBoost basic algorithm are 96.26%, 90.91%, 0.860 2 and 0.970 5, respectively, which higher than GBDT, LightGBM and Adaboost.
From 2013 to 2020, the overall development degree of landslides in the study area is relatively high, and the spatial distribution of landslides is uneven among villages and towns, and the landslides is distributed on both sides of the river valley and the river, showing the characteristics of regional concentration. From 2013 to 2020, Miaowan landslide has high development intensity, strong activity, repeated resurrection phenomenon and induced new landslides. The slope of the landslide is about 25°-45°, and it’s topographic characteristics change little, significant changes mainly focus on color, texture, geometry and vegetation coverage.
Pixel offset tracking (POT) for optical remote sensing imagery is widely used to invert coseismic deformation fields and monitor landslides. Traditional pixel offset tracking method estimates the displacement of the central pixel by searching for the matching window with the highest correlation, which is computationally inefficient and suffers from inaccurate deformation boundary extraction due to the decoherence effects in the region with dynamical deformation. We introduce the optical flow field model commonly used in computer vision to the pixel offset tracking problem to obtain accurate surface deformation efficiently.
The optical flow field method applicable to optical remote sensing images and the improved inversion algorithm for the time series analysis are proposed to inverse the surface deformation. Experiments on the simulated coseismic deformation fields in Tajikistan are detailed to assess the feasibility and the minimum detectable deformation of the optical flow field method. The advantages of the proposed method over computational cost and deformation boundary extraction accuracy are illustrated by the co-seismic deformation field of the California earthquake and the displacement of the Baige landslide. Furthermore, the performance on estimating large gradient deformation and the robustness of the improved time series inversion algorithm are discussed by analyzing the time series deformation of the Baige landslide.
The results show that compared with the traditional window correlation matching method, the optical flow field method has an offset tracking accuracy of 0.032 pixel, which improves the computational efficiency by about 20 times, and the accuracy of the deformation zone is improved by 25.9%. The time series weighted inversion algorithm reduces the uncertainties in the estimation of east-west and north-south displacements of optical remote sensing images by 16.2% and 12.4%, respectively.
The proposed method alleviates the pixel offset tracking problem in the boundary region with large gradient deformation.
Mine slope instability is one of the main factors restricting the safety production of open-pit mines in China. Ground-based synthetic aperture radar interferometry technology has been gradually introduced into the application of slope safety monitoring and early warning prediction in open-pit mines. However, the high-frequency rolling update characteristics of ground radar interferometry data lead to large data error accumulation and unobvious curve mutation characteristics.
Processing the original data by dislocation subtraction and velocity reciprocal method can effectively reduce the vibration of high-frequency data, improve the readability of critical sliding data. After data processing, it can highlight the trend characteristics of key deformation data. The research is based on the analysis of cumulative displacement curve, velocity curve and reciprocal velocity curve group treated with different periods.
It is found that there are three characteristic points in the curve group: Sudden deformation increase point, velocity increase point and stable vibration point. Through these characteristic points, the slope landslide disaster can be predicted. The trend of key deformation data can be highlighted by using the three feature points of deformation sudden increase point, velocity growth point and stable vibration point.
Through the identification of three feature points, the possible landslide can be effectively identified in advance and the landslide time can be predicted, which provides a new technical path and solution for landslide early warning and prediction analysis based on ground-based interferometric radar.
Land subsidence caused by long-term over-exploitation of groundwater is one of major problems in Beijing. Since the opening of the South-to-North Water Transfer Project, the problem of water shortage in Beijing has been greatly alleviated, and the pressure of land subsidence has been reduced to a certain extent.
In order to analyze the development of land subsidence after the start of the South-to-North Water Transfer in Beijing, ascending and descending time-series interferometric synthetic aperture radar (InSAR) technique is used to monitor land subsidence in Beijing. First, the mean deformation velocity and cumulative deformation in line of sight in Beijing from January 2015 to December 2020 is obtained by the small baseline subset InSAR. Second, the robust least square fitting method is used to fuse the deformation results of the lifting rail, after that the global positioning system monitoring data are compared with the fusion results of lifting rail. Finally, the variation trend between the deformation results obtained by the robust least quadratic fitting and groundwater data is analyzed.
The deformation results show that the center of Beijing is basically stable and the deformation distribution is not uniform. The maximum ascending annual deformation velocity and the maximum ascending cumulative deformation amount reach -134 mm/a and -697 mm respectively. The maximum descending annual deformation velocity and the maximum descending cumulative deformation amount reach -135 mm/a and -734 mm respectively. And the fusion results obtained by the least square fitting method has reliability and accuracy.
The subsidence rate in Beijing shows a decreasing trend with the gradual increase of groundwater level. In general, the middle route of South-to-North Water Transfer Project has alleviated the expansion trend of land subsidence in Beijing to a certain extent.
In the research work of reservoir landslide displacement prediction, due to the lag of reservoir water level response, it is difficult for the traditional landslide displacement prediction model to analyze the monotonically increasing step deformation characteristics, which seriously affects the prediction results, and it is necessary to establish a landslide displacement prediction model that can consider the time lag effect.
We analyze the time lag effect of reservoir levels separately through grey correlation, account for the cumulative effect of earlier rainfall, and consider the effect of earthquake on landslide deformation, and establishe an autoregressive distributed lag landslide displacement prediction model that can be applied to engineering sites.
The results show that: (1) The engineering case study concluded that rising reservoir levels and earthquakes were the main triggering factors for the increased deformation of the landslide, and the lag time of reservoir levels acting on landslide deformation was 8 days. (2) The correlation coefficient between the cumulative landslide displacement and the actual displacement calculated by the new model is as high as 0.992 7, with a root mean square error of 14.11 mm. (3) The calculation of trend speed ratio indicators can provide a new sensitivity evaluation parameter for landslide monitoring and early warning.
The study establishes a physically significant prediction model for reservoir bank landslide displacements, provides a comprehensive analysis of landslide displacements, achieves a quantitative calculation of the seismic contribution to landslide displacement evolution, and provides new technical support for the safety risk management of the whole process of reservoir bank landslide evolution during the water storage period.
As a continuation of mining subsidence, the surface secondary subsidence (including sinking and uplift) of closed mines poses a potential threat to the safety of surface buildings-structures in mining areas. However, at this stage, the law analysis of the surface secondary subsidence of closed mines under different geological mining conditions is not yet comprehensive. Due to the high underground water level, thick Quaternary loose layers, and multiple coal seams in the Huainan mining area, monitoring and analyzing the surface secondary subsidence of closed mines in the Huainan mining area has important theoretical and practical values.
First, in order to verify the reliability of the monitoring results, the StaMPS software is employed to simultaneously perform persistent scatterers interferometry and small baseline subset processing. Amplitude dispersion index and amplitude difference dispersion index are used to select coherent points, respectively. Then, the unwrapped phases of the coherent points are obtained by a three-dimensional phase unwrapping algorithm. Finally, the surface subsidence of the coherent points are obtained by using temporal-domain low-pass and spatial-domain high-pass filtering.
The results show that: (1) The surface uplifts after the closure of Xinjisan and Lizuizi-Xinzhuangzi-Xieyi mines, with a maximum uplift rate of 51.1 mm/a, and is located in Xinzhuangzi mine, while the surface of Panyi mine is still sinking due to its late closure in September 2018. (2) The surface secondary subsidence law of closed mines in Huainan mining area is sinking stage⁃stable stage⁃uplift stage, which is consistent with the surface subsidence law of closed mines in Xuzhou mining area, but the sinking, stable and uplift stages do not necessarily occur successively over time. (3) There may be a hydraulic connection between Lizuizi, Xinzhuangzi, and Xieyi mines. The groundwater first rises from the junction of Xinzhuangzi and Xieyi mines and then flows to the southeast and northwest sides.
Although the law of surface subsidence in the closed mines of Huainan and Xuzhou mines is relatively consistent, there are differences in the law of surface uplift due to different hydrogeological mining conditions. Therefore, in future work, we will continue to pay attention to the law of surface secondary subsidence of closed mines under different geological mining conditions.
The landslides disaster is one of the most important factors for the construction of major infrastructure in the western China. How to effectively and reliably carry out wide-area landslide susceptibility prediction has always been a frontier difficulty in domestic and foreign studies. However, the present data-driven methods for prediction of landslide susceptibility in large-scale and complex scenarios still face two major issues:(1) The difference between various landslide inducing environments in a wide range of scenarios, would cause the difficulty to applying a single model to account for multiple landslide phenomenon;(2) small sample problem: Complex environmental tasks require models with large capacity and strong representative power, but there is a lack of sufficient landslide samples in practice.
In response to the above problems, this paper takes Qijiang and Fuling District of Chongqing City, China as an example, proposes a local prediction strategy, and introduces the idea of meta-training an intermediate representation suited to be generalized, that can be adapted to the landslide sensitivity prediction task cor-responding to the current local area, with only a very small number of samples in the local area, and number of iterations. Thus, the two issues mentioned can be well settled.
The proposed method is different from traditional methods such as support vector machines, multilayer perceptrons, and random forests, which require a large number of samples and gradient iterations to train the supervised model. Instead, only a small sample is required to fine-tune the intermediate model, which still improves the global accuracy by 1%-5%, the precision by 1%-3%, the
The meta-learning paradigm enables few-shot adaptation of landslide susceptibility prediction model with superior statistical performance.
With the development of Shenzhen city, China, land renovation is more frequent. At the same time, affected by the subtropical monsoon climate, the area under the jurisdiction has abundant rainfall and dense vegetation coverage, making it difficult to identify the hidden dangers of geological hazards widely distributed on artificial slopes and natural slopes. Therefore, it is necessary to develop a set of hazard evaluation system of geological disaster that can solve the unique terrain and climate conditions in Shenzhen, so as to achieve the purpose of preventing disasters in advance and reducing casualties.
(1) On the basis of high-precision digital elevation model of Shenzhen city obtained by airborne light detection and ranging(LiDAR), about 3 500 slope disaster prone points in Shenzhen are obtained through data collection, remote sensing interpretation and field verification. The sample library expanded 330% after proofreading.(2) Taking 3 major factors (8 factors) of terrain, geological structure and human engineering activities into comprehensive consideration, and based on the rainfall-induced disaster mechanism, a rainfall collection factor is proposed, and the weight of evidence method is used to complete the geological disaster hazard evaluation model under rainfall-induced conditions.(3) The threshold determination method of“key point control”under the actual background of single disaster is proposed, and the classification of the risk assessment model is completed.
The area under curve value of receiver operating characteristic curve model reaches 0.903, indicating that the model has a good effect on disaster forecasting. LiDAR technology can improve the identification accuracy of geological hazards in cities under dense vegetation coverage.
Based on airborne LiDAR data, through a series of means such as expansion of disaster database, analysis of disaster distribution law, establishment of disaster evaluation factors, and classification of risk levels, it can form a refined evaluation system for the hazard evaluation of the slope in densely vegetated areas under the influence of the subtropical monsoon climate.
Because of high frequency of extreme weather, railway subgrade disaster shows a trend of increasing, great harmfulness and is hard to detect in advance. Even a small-size subgrade disaster may cause railway paralysis, which prevents the development of transportation. Many incidents illustrate that the earliest subgrade disaster always appeared in areas where no case ever reported before. It means that traditional monitoring methods are hard to detect hidden danger area. Thus, a promising method is urgent to be proposed for meeting the monitoring requirement for railway safety.
First, the characteristics of subgrade disaster are summarized under different disaster-pregnant environments, and the advantages of various monitoring methods are discussed to find a collaborative applications method for early identification of subgrade disaster. The potential hazards that may cause damage to subgrade structure and the deterioration degree of subgrade are considered as the main monitoring object of subgrade service status, including subgrade deformation monitoring, structural health monitoring, geological hazard monitoring along the railway, track irregularity, and external environmental monitoring. Second, an investigative approach based on the integration of space-air-train-ground muti-source techniques is proposed to detect the geohazards and monitor the service status of railway subgrade. It means that collaborative application of different monitoring methods, cooperative analysis of different scales and resolutions data and collaboration of between various railway departments are required. Two monitoring schemes for subgrade disasters and service status of subgrade are proposed based on the integration of multi-source and multi-scale monitoring technique. Finally, the development direction of subgrade service status monitoring is discussed.
This monitoring system has been applied in the identification of subgrade service status in the Shanghai-Nanjing high speed railway, which can quickly investigate the location of the disasters and the deterioration degree of subgrade.
On September 20, 2019, the Jungong ancient landslide in Lajia Town, Maqin County, Qinghai Province, China suffered a local failure, which led to the interruption of traffic and seriously threatened the safety of local residents' lives and properties. It is urgent to find out the deformation area and deformation law of the ancient landslide, and to provide the support for prevention and control enginee-ring design, monitoring and early warning.
First, using high-resolution satellite images, digital elevation model data and combined with field investigations, the ancient landslide were zoned based on landslide morphological characteristics and signs of deformations; further, Sentinel-1 radar satellite descending data from January 2017 to December 2020 were used to analyze the surface deformation characte-ristics and deformation patterns of the ancient landslide based on small baseline subset interferometric synthetic aperture radar technology (SBAS-InSAR).
Based on the morphological characteristics and deformation signs of the landslide, the ancient landslide was divided into four sub-areas. The SBAS-InSAR results show that the ancient landslide is in a continuous slow creeping state. The strong deformation area of the landslide is mainly located in the road excavation section. Human activities greatly disturb the stability of the ancient landslide. The deformation rate of the strong deformation area of the landslide has a good relationship with rainfall.
Although the ancient landslide has been partially treated with anti-slide piles and other projects, the ancient landslide has multi-level sliding surfaces, and the depth of the existing anti-slide piles is not enough. Although the ancient landslide has played a certain role in anti-slide, it has not completely prevented the creep deformation of the landslide. It is suggested that the subsequent treatment projects should use drilling and other exploration techniques to find out the depth of the multi-level sliding surfaces before designing, and installing on-site real-time monitoring and early warning equipment such as crack meters in the strongly deformed areas. Combined with the medium and long-term monitoring of radar satellite InSAR, a point-surface monitoring and early warning system could be realized.
The continuous exploitation of the Liaohe Delta oilfield has resulted in severe surface subsidence, impacting oil recovery rates, production operations, and posing threats to surface infrastructure and ecological environments. To ensure the safe exploitation of underground fluid resources and protect the regional environment, monitoring implementation is needed for this region.
The method of small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technology with coherence information as weights was used to analyze the surface deformation. Using the fusion decomposition of synthetic aperture radar descending and ascending orbit results to extract both vertical and horizontal east-west deformation within the Shuguang oilfield area. Subsequently, a reservoir compaction-induced subsidence inversion model is applied to the Shuguang oilfield to simulate and interpret the observed subsidence phenomena, linking them to the underground fluid resource exploitation activities.
The results reveal significant ground subsidence throughout the Liaohe Delta region, particularly in the Shuguang oilfield and Huanxiling oilfield. The average line of sight subsidence rates reaching 158 mm/a and 73 mm/a, respectively. In the Shuguang oilfield, there is horizontal movement towards the subsidence center, with approximately equal magnitudes of movement on the east and west sides. The maximum horizontal movement rate is observed to be -62 mm/a (westward motion). Furthermore, the reservoir compaction and subsidence model based on Shuguang oilfield reservoir parameters effectively invert the maximum subsidence position at the center of the oilfield, with subsidence range and magnitude consistent with the InSAR observation results.
The study concludes that continuous exploitation of oil has led to significant ground subsidence in the Liaohe Delta region, especially in Shuguang oilfield and Huanxiling oilfield, with clear patterns of subsidence and horizontal movement detected using SBAS-InSAR technology. The developed reservoir compaction-induced subsidence inversion model proves effective in simulating subsidence phenomena associated with oilfield operations. These findings underscore the importance of monitoring and managing subsidence risks to ensure the safe exploitation of underground resources and to protect regional ecological environments in the Liaohe Delta region.
Geological hazard points and hidden danger points are the data basis for geological hazard evaluation, while the existing records of geological hazard points have poor timeliness and are incomplete. To solve this problem, the deformation information obtained by multi-temporal interferometric synthetic aperture radar (InSAR) was integrated into the geological hazard evaluation model. And we explore how to make better use of the deformation information.
The greater the deformation level, the greater the possibility of geological hazards. This paper not only takes the deformation points obtained by multi-temporal InSAR as the geological hazard points/hidden danger points, but also integrates the deformation level information obtained by multi-temporal InSAR as an evaluation factor into the susceptibility evaluation model, making full use of the effective deformation information obtained by multi-temporal InSAR. And the evaluation model adopts the coupling model based on information value model and the analytic hierarchy process model to obtain the susceptibility evaluation and zoning of the geological hazards in Baiyin City, Gansu Province,China.
Through the verification of the existing geological disaster point data, it is found that the partitions in this paper are in good agreement with the existing geological hazard points distribution.In the designated extremely high-prone areas, there are nearly 8 geological disaster points within 10 km2, while less than one in the extremely low-prone areas.
The multi-temporal InSAR deformation information added to the geological hazard evaluation model greatly improves the timeliness and quantity of records of geological hazard points/hidden points. However, only one kind of synthetic aperture radar data cannot completely identify all geological hazard points/hidden danger points, due to the limitations of incidence angle and microwave wavelength. In the futher work, we will focus on the combination of multiple deformation monitoring technologies to jointly monitor surface deformation, such as multi-sensor and multi-track InSAR technology, airborne light laser detection and ranging and high-resolution optical remote sensing.
Flash floods are natural disasters caused by sudden rise in water levels in mountainous rivers, which are characterized by instantaneity and great destructiveness. In recent years, the frequent occurrence of flash floods in Longnan city, Gansu province, has posed a serious threat to the safety of local people's lives and property, thus it is urgent to carry out a risk assessment of flash floods in this region.
This study takes Longnan city as the study area, and utilizes the MaxEnt model combining with the particle swarm algorithm to evaluate the vulnerability of study area based on 834 flash flood hazard points investigated and 32 disaster-causing factors. It also predicts the spatial pattern changes and potential mass migration trends of the future flash flood vulnerability areas based on three periods of climate data from the current period (2021—2040) and the future period (2041—2060, 2061—2080, 2081—2100).
The area under receiver operating characteristic curve of the results of the study in each period is above 0.85, which indicates that the precision of the results of the method is good. The main cause factors in this study area are driest month precipitation, monthly mean diurnal temperature difference, coefficient of variation of precipitation, warmest month maximum temperature, land use, distance from the river, soil texture, profile curvature, elevation, and topographic relief. The flash flood-prone areas in the study area varies in different periods, but are mainly distributed in Wudu District, Wen County and Tanchang County, and the simulation results for the three future periods (2041—2060, 2061—2080, 2081—2100) reflected a decreasing trend compared with the current period (2021—2040).
Flood disaster is a very destructive natural disaster. The main reasons for its generation include heavy rainfall, storm surge and dam break. When flood disaster occurs in populated areas such as cities and towns, the flood will directly threaten the safety of life and property of residents. Also it will cause paralysis of land and underground transportation, interruption of water and electricity transportation. In the process of flood rescue, quick and accurate identification of flooded roads is conducive to planning appropriate personnel transfer and material transportation routes, and reducing subsequent losses caused by floods. Aiming at the problem that roads in the flood disaster scenario cannot be automatically and correctly identified, this paper proposes an end-to-end flooded road detection method based on deep learning.
The proposed method uses a three-branch encoder-decoder structure and uses strip convolution, in which the efficient extraction of linear features is realized. And the coordination dual attention mechanism can effectively guide the network, and realize the identification of road areas. The method can effectively utilize the historical optical remote sensing images before the disaster and the real-time optical remote sensing during the disaster. The image is applied to detect the flooded and non-flooded roads in the disaster-stricken area, and a comparative experiment is carried out on the self-built dataset.
The experimental results show that the precison and recall rate are 0.838 1 and 0.666 8 on pre-disaster road, 0.796 6 and 0.607 4 on post-disaster road, 0.780 0 and 0.661 4 on affected road respectively.
The proposed method has achieved the goal of automatically identifying the flooded roads in the flood-stricken area. The ability of detecting flooded roads and non-flooded roads can provide strong support for flood disaster rescue and reduce losses of life and property caused by flood disasters.
Convolutional neural network (CNN) was widely used in landslide susceptibility assessment because of its powerful feature extraction capability. However, with the demand for scene diversification and high accuracy, the algorithm of CNN was constantly improved. The practice of improving accuracy by deepening the network hierarchy or combining other models tended to increase the number of model parameters and the amount of calculation greatly, which led to the difficulty of model training or the overfitting of results, thus limiting its practical application. We want to solve the above problems from the model structure.
We proposed to build a multi-dimensional CNN coupling model. Through the asymmetric aggregation of feature maps, one-dimensional CNN(1D-CNN) and two-dimensional CNN(2D-CNN) were connected to maintain network depth, limit model parameters, and reduce computation. The parameters sharing of multi-dimensional convolution kernels was used to capture the deep coupling features of different dimensions and different landslide factors, so as to make full use of features and avoid overfitting. Taking Sedongpu gully in Xizang,China as the experimental area, this paper selected 11 kinds of landslide influencing factors to analyze the landslide susceptibility.
The results show that the multi-dimensional CNN coupling structure had the same training efficiency as the shallow 2D-CNN with fewer parameters due to the reduced computational effort. While compared with the deep 2D-CNN with the approximate number of parameters, the training time of the proposed method was significantly reduced and the training cost was lower. In addition, the coupled model had a stronger feature learning ability than the independent 1D-CNN and 2D-CNN, and therefore obtained higher model accuracy. Under each confusion matrix metric of the testing data, the coupled model received higher scores, and thus obtained more reliable landslide susceptibility assessment results.
The multi-dimensional CNN coupling model proposed in this paper is a reliable method applicable to landslide susceptibility assessment. This study provides new theoretical guidance and technical support for further landslide hazard monitoring and prevention.
The response law of ancient (old) landslides in the reservoir area is an important research topic. Previous research primarily analyzed real-time surface displacement and reservoir water level data. However, professional monitoring conditions are often lacking on most reservoir bank slopes. This complicates tracking the landslide's historical deformation. Satellite and airborne remote sensing platforms enable multi-scale, long-term monitoring of landslide deformation and damage.
This study employs multi-source three-dimensional observation technologies including aerial, space-based, and terrestrial platforms to monitor the deformation and evolution of the Pubugou Hydropower Station's Hongyanzi landslide over approximately 10 years. It utilizes unmanned aerial vehicle photography (optical imaging) and light detection and ranging (LiDAR) for detailed topographic mapping and deformation analysis from 2009 to 2020. Additionally, time-series interferometric synthetic aperture radar (InSAR) technology is used to track long-term surface deformations from October 2014 to July 2020. Field investigations have identified typical deformation and failure characteristics of the landslide, incorporating geological conditions and external factors such as rainfall and reservoir water levels to analyze causal mechanisms and dynamic trends.
The irregularly semicircular Hongyanzi landslide spans 20-50 m in thickness, encompasses approximately 15.53 million m³ in volume, and slides at an approximate bearing of 340°. Composed of quaternary pebbled stones, silty sand, and clay, the landslide's bed slopes between 20° and 25°. Its lithology includes Emeishan Formation basalt and Yangxin Formation dolomite. Existing since 2006 or earlier, the landslide features elements like walls and steps. LiDAR imagery from 2009 clearly delineates its boundaries, though it shows no new signs of deformation or failure. Following reservoir impoundment, the reactivated landslide develops new, widening cracks along its rear edge. Post-reactivation, the landslide predominantly undergos uniform deformation, with more significant movement at the trailing edge than the leading edge, without marked acceleration. Heavy rainfall is the most significant control factor, imparting stepwise deformation characteristics to the landslide.
A comprehensive analysis of multi-source data reveals that phenomena like the Hongyanzi landslide exhibit typical long-term, gradual, and seasonal movements. Long-term InSAR effectively captures these characteristics. Multi-stage optical remote sensing and surface point cloud data from LiDAR, after vegetation removal, enable more intuitive comparisons of macroscopic deformation across different landslide areas. Integrating this with geological assessments and field investigations allows for detailed engineering analyses to ascertain the causes, patterns, and future trends of landslide activity.