Preliminary Analysis of Extreme Rainfall-Induced Cluster Landslides in Jiangwan Township, Shaoguan, Guangdong, April 2024
-
摘要:
2024年4月中下旬,广东省韶关市发生极端强降雨事件,在韶关江湾镇地区诱发大量滑坡灾害,造成部分地区持续断联近36 h,引起了社会的广泛关注。快速准确地查明滑坡基本特征、发育分布规律及形成条件对于灾害应急决策和风险隐患排除处置至关重要。利用灾后的光学遥感影像并结合深度学习模型,对韶关江湾镇降雨诱发滑坡进行了快速智能识别与人工校核,共解译出1 192处滑坡,总面积约3.14 km²。滑坡规模以中小型滑坡为主,主要沿河流呈北东-南西向聚集带状分布,群发性效应显著。空间统计分析表明, 滑坡主要分布在200~300 m高程范围内、坡度为10°~30°的凹坡上。进一步使用随机森林模型与SHAP理论对滑 坡的地貌主控因子进行量化分析,发现不同地形地貌因子对滑坡形成均有不同程度的非线性影响,高程、坡度和汇水条件等多因素耦合作用共同控制了滑坡的形成。该研究突出了基于深度学习的智能识别与分析技术在滑坡灾害应急调查与形成条件分析中的巨大优势,可为灾害损失快速评估和风险隐患排查提供重要技术支撑。
Abstract:ObjectivesIn 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.
MethodsUsing 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.
ResultsAfter 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.
ConclusionsThis 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.
-
http://ch.whu.edu.cn/cn/article/doi/10.13203/j.whugis20240202
-
![]()
图 1 韶关市江湾镇2024年4月强降雨诱发典型滑坡灾害
Figure 1. Typical Landslide Disaster Induced by Heavy Rainfall in April 2024 in Jiangwan Town, Shaoguan City
![]()
图 5 研究区滑坡前后Planet遥感影像
Figure 5. Pre- and Post- Landslide Planet Images of the Study Area
![]()
图 7 因子筛选前后的Pearson相关系数
Figure 7. Pearson Correlation Coefficients Before and After Factor Filtration
![]()
图 10 江湾镇降雨诱发群发性滑坡密度图
Figure 10. Density Map of Rainfall-Induced Cluster Landslide in Jiangwan Town
![]()
图 11 江湾镇群发性滑坡与地形地貌因子的关系
Figure 11. Relationship Between Cluster Landslides and Geomorphologic Factors in Jiangwan Town
![]()
图 13 地形地貌因子对滑坡的贡献
Figure 13. Contribution of Topographic and Geomorphic Factors to Landslide
-
[1] 王欣, 方成勇, 唐小川, 等. 泸定Ms 6.8地震诱发滑坡应急评价研究[J]. 武汉大学学报（信息科学 版）, 2023, 48（1）： 25-35. Wang Xin, Fang Chengyong, Tang Xiaochuan, et al. Research on Emergency Evaluation of Landslides Induced by the Luding Ms 6.8 Earthquake[J]. Geomatics and Information Science of Wuhan University, 2023, 48（1）： 25-35.
[2] 李为乐, 陈俊伊, 陆会燕, 等. 泸定Ms 6.8地震对海螺沟冰川的影响应急分析[J]. 武汉大学学报（信息科学版）, 2023, 48（1）： 47-57. Li Weile, Chen Junyi, Lu Huiyan, et al. Emergency Analysis of the Impact of the Luding Ms 6.8 Earthquake on Hailuogou Glacier[J]. Geomatics and Information Science of Wuhan University, 2023, 48（1）： 47-57.
[3] 许强, 董秀军, 李为乐. 基于天-空-地一体化的重大地质灾害隐患早期识别与监测 预警[J]. 武汉大学学报（信息科学版）, 2019, 44（7）： 957-966. Xu Qiang, Dong Xiujun, Li Weile. Integrated Space-Air-Ground Early Detection, Monitoring and Warning System for Potential Catastrophic Geohazards[J]. Geomatics and Information Science of Wuhan University, 2019, 44（7）： 957-966.
[4] 巨袁臻, 许 强, 金时超, 等. 使用深度学习方法实现黄土滑坡自动识别[J]. 武汉大学学报（信息科学版）, 2020, 45（11）： 1747-1755. Ju Yuanzhen, Xu Qiang, Jin Shichao, et al. Automatic Object Detection of Loess Landslide Based on Deep Learning[J]. Geomatics and Information Science of Wuhan University, 2020, 45（11）： 1747-1755.
[5] 许强, 郭晨, 董秀军. 地 质灾害航空遥感技术应用现状及展望[J]. 测绘学报, 2022, 51（10）： 2020-2033. Xu Qiang, Guo Chen, Dong Xiujun. Application Status and Prospect of Aerial Remote Sensing Technology for Geohazards[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51（10）： 2020-2033.
[6] Martha T R, Kerle N, Jetten V, et al. Characterising Spectral, Spatial and Morphometric Properties of Landslides for Semi-automatic Detection Using Object-Oriented Methods[J]. Geomorphology, 2010, 116（1/2）： 24-36.
[7] Ullo S L, Mohan A, Sebastianelli A, et al. A New Mask R-CNN-Based Method for Improved Landslide Detection[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14： 3799-3810.
[8] 杨昭颖, 韩灵怡, 郑向向, 等. 基于卷积神经网络的遥感影像及 DEM滑坡识别： 以黄土滑坡为例[J]. 自然资源遥感, 2022, 34（2）： 224-230. Yang Zhaoying, Han Lingyi, Zheng Xiangxiang, et al. Landslide Identification Using Remote Sensing Images and DEM Based on Convolutional Neural Network： A Case Study of Loess Landslide[J]. Remote Sensing for Natural Resources, 2022, 34（2）： 224-230.
[9] 胡瑞林, 范林峰, 王珊珊, 等. 滑坡风险评价的理论与方法研究[J]. 工程地质学报, 2013, 21（1）： 76-84. Hu Ruilin, Fan Linfeng, Wang Shanshan, et al. Theory and Method for Landslide Risk Assessment-Current Status and Future Development[J]. Journal of Engineering Geology, 2013, 21（1）： 76-84.
[10] 刘海知, 徐辉, 包红军, 等. 机器学习分类算法在降雨型滑坡预报中的应用[J]. 应用气象学报, 2022, 33（3）： 282-292. Liu Haizhi, Xu Hui, Bao Hongjun, et al. Application of Machine Learning Classification Algorithm to Precipitation-Induced Landslides Forecasting[J]. Journal of Applied Meteorological Science, 2022, 33（3）： 282-292.
[11] 黄发明, 胡松雁, 闫学涯, 等. 基于机器学习的滑坡易发性预测建模及 其主控因子识别[J]. 地质科技通报, 2022, 41（2）： 79-90. Huang Faming, Hu Songyan, Yan Xueya, et al. Landslide Susceptibility Prediction and Identification of Its Main Environmental Factors Based on Machine Learning Models[J]. Bulletin of Geological Science and Technology, 2022, 41（2）： 79-90.
[12] Talaat F M, ZainEldin H. An Improved Fire Detection Approach Based on YOLO-v8 for Smart Cities[J]. Neural Computing and Applications, 2023, 35（28）： 20939-20954.
[13] Inan M S K, Rahman I. Explainable AI Integrated Feature Selection for Landslide Susceptibility Mapping Using TreeSHAP[J]. SN Computer Science, 2023, 4（5）： 482.
[14] 王全涛, 方敏. 广东省韶关市曲江区地质灾害分区及防治[J]. 地质与资源, 2017, 26（1）： 73-76. Wang Quantao, Fang Min. Zonation and Control of Geological Disasters in Qujiang District of Shaoguan City, Guangdong Province[J]. Geology and Resources, 2017, 26（1）： 73-76.
[15] 刘伟旭, 吴亮. 武江区滑坡和崩塌地质灾害形成条件浅析[J]. 冶金与材料, 2022, 14（1）： 135-136. Liu Weixu, Wu Liang. Analysis on the Formation Conditions of Landslide and Collapse Geological Disasters in Wujiang District[J]. Metallurgy and Materials, 2022, 14（1）： 135-136.
[16] 吴洁玲, 查轩, 陈世发, 等. 1951—2018年韶关不同量级降雨侵蚀力变化[J]. 水土保持学报, 2021, 35（4）： 21-26. Wu Jieling, Zha Xuan, Chen Shifa, et al. Variations of Rainfall Erosivity of Different Magnitudes in Shaoguan from 1951 to 2018[J]. Journal of Soil and Water Conservation, 2021, 35（4）： 21-26.
[17] Xu Y L, Ouyang C J, Xu Q S, et al. CAS Landslide Dataset： A Large-Scale and Multisensor Dataset for Deep Learning-Based Landslide Detection[J]. Scientific Data, 2024, 11： 12.
[18] Li H, Zhao J Y, Yan B Q, et al. Global DEMs Vary from one to Another： An Evaluation of Newly Released Copernicus, NASA and AW3D30 DEM on Selected Terrains of China Using ICESat-2 Altimetry Data[J]. International Journal of Digital Earth, 2022, 15（1）： 1149-1168.
[19] 魏林森, 丁宏伟, 王婷, 等. 降水对陇西黄土滑坡的诱发作用及时空影响分析[J]. 冰川冻土, 2017, 39（3）： 609-615. Wei Linsen, Ding Hongwei, Wang Ting, et al. Loess Landslide in Longxi of Gansu Province： Precipitation Inducted Function and Space-Time Effect[J]. Journal of Glaciology and Geocryology, 2017, 39（3）： 609-615.
[20] Wang G L, Li T L, Xing X L, et al. Research on Loess Flow-Slides Induced by Rainfall in July 2013 in Yan’an, NW China[J]. Environmental Earth Sciences, 2015, 73（12）： 7933-7944.
[21] 李为乐, 许强, 李雨森, 等. 2023年积石山Ms6.2级地震同震地质灾害初步 分析[J]. 成都理工大学学报（自然科学版）, 2024, 51（1）： 33-45. Li Weile, Xu Qiang, Li Yusen, et al. Preliminary Analysis of the Coseismic Geohazards Induced by the 2023 Jishishan Ms 6.2 Earthquake[J]. Journal of Chengdu University of Technology （Science & Technology Edition）, 2024, 51（1）： 33-45.
[22] 范宣梅, 王欣, 戴岚欣, 等. 2022年Ms 6.8级泸定地震诱发地质灾害特征与空间分布规律研究[J]. 工程地质学报, 2022, 30（5）： 1504-1516. Fan Xuanmei, Wang Xin, Dai Lanxin, et al. Cha-racteristics and Spatial Distribution Pattern of Ms 6.8 Luding Earthquake Occurred on September 5, 2022[J]. Journal of Engineering Geology, 2022, 30（5）： 1504-1516.
[23] Hesterberg T. Bootstrap[J]. WIREs Computational Statistics, 2011, 3（6）： 497-526.
[24] Taalab K, Cheng T, Zhang Y. Mapping Landslide Susceptibility and Types Using Random Forest[J]. Big Earth Data, 2018, 2（2）： 159-178.
[25] 刘伟淇, 张家铭. 竹溪县滑坡灾害易发性分区评价[J]. 自然灾害学报, 2024, 33（1）： 175-185. Liu Weiqi, Zhang Jiaming. Zoning Evaluation of Landslide Hazard Susceptibility in Zhuxi County[J]. Journal of Natural Disasters, 2024, 33（1）： 175-185.
[26] Jain S, Khosa R, Gosain A K. Impact of Landslide Size and Settings on Landslide Scaling Relationship： A Study from the Himalayan Regions of India[J]. Landslides, 2022, 19（2）： 373-385.
[27] Song Jingyan, Liu Yang, Dong Xiujun, et al. Spatial Distribution of Earthquake-Induced Landslide in Densely Populated Area of Luding 9.5 Earthquake[J]. Bulletin of Geological Science and Technology, 2024,DOI： 10.19509/j.cnki.dzkq.tb20230619.（宋静园, 刘洋, 董秀军, 等. “9.5”泸定地震人口密集区域同震滑坡空间分布规律[J]. 地质科技通报, 2024,DOI：10.19509/j.cnki.dzkq.tb20230619.） doi: 10.19509/j.cnki.dzkq.tb20230619 [28] 鲁晓, 祁生文, 郑博文, 等. 川藏交通廊道崩滑灾害分布及其危险性评价[J]. 工程地质学报, 2023, 31（3）： 718-735. Lu Xiao, Qi Shengwen, Zheng Bowen, et al. Distribution and Hazard Assessment of Collapses and Landslides in Sichuan-Tibet Traffic Corridor[J]. Journal of Engineering Geology, 2023, 31（3）： 718-735.
[29] Huang J H, Wen H J, Hu J W, et al. Deciphering Decision-Making Mechanisms for the Susceptibility of Different Slope Geohazards： A Case Study on a SMOTE-RF-SHAP Hybrid Model[J]. Journal of Rock Mechanics and Geotechnical Engineering, 2024, DOI：10.1016/j.jrmge.2024.03.008. doi: 10.1016/j.jrmge.2024.03.008
[30] 孔嘉旭, 庄建琦, 彭建兵, 等. 兰州老狼沟黄土微地貌灾害链时空分布 特征与危险性模拟研究[J]. 工程地质学报, 2021, 29（5）： 1401-1415. Kong Jiaxu, Zhuang Jianqi, Peng Jianbing, et al. Spatial and Temporal Distribution Characteristics and Risk Simulation of Loess Micro Geomorphic Disaster Chain in Laolang Gully, Lanzhou[J]. Journal of Engineering Geology, 2021, 29（5）： 1401-1415.
[31] 赵萍, 赵 思逸, 孙雨, 等. 基于斜坡单元和语义分割的皖南地区滑坡灾害易发性评估[J]. 地质科学, 2024, 59（2）： 562-574. Zhao Ping, Zhao Siyi, Sun Yu, et al. Landslide Susceptibility Assessment in Southern Anhui Province Based on Slope Units and Semantic Segmentation[J]. Chinese Journal of Geology, 2024, 59（2）： 562-574.
下载:
计量
- 文章访问数: 844
- HTML全文浏览量: 141
- PDF下载量: 263
