Spatial Distribution and Identification of Hidden Danger Points of Landslides Based on Geographical Factors

  Objectives  407 hidden danger points of landslides and geological structures, topography, and human activities data were used to study the disaster-pregnancy environment in Xiangxi Autonomous Prefecture, the spatial and temporal distribution of landslide hazards and its correlation with the geological envi-ronment, to achieve quantitative analysis of the spatial distribution characteristics of landslides in Xiangxi Autonomous Prefecture; to verify the accuracy of XGBoost applied to the classification of landslide susceptibility and to analyze the topography, geological conditions, precipitation, human activities and other factors and landslide hazards.

  Methods   According to the geographical census data, use proximity analysis to form the distance data of roads, buildings, structures, artificial piles, and bare landslide points; use DEM (digital elevation model) data and spatial analysis tools to calculate the slope, aspect, and curvature of the study area data; area data includes cultivated land area, forest land area, road area, water area, building area, structure area, artificial pile area and bare land area, referring to cultivated land, forest land, road, areas of waters, buildings, structures, artificial piles and bare grounds; use extraction tools to obtain precipitation data for each hidden danger point of the landslide from precipitation data with a spatial resolution of 1° × 1°; pass the band calculation and eliminate invalid value to get NDVI(normalized differential vegetation index) data; use the ArcGIS extraction tool to obtain the soil moisture at each landslide point. By counting the number of hidden trouble spots in different elevations, slopes, vegetation coverage and other areas, the spatial distribution characteristics of hidden trouble spots of landslides are analyzed. At the same time, 1 020 sample points in Xiangxi Autonomous Prefecture were selected, of which 407 were landslide disaster points, a binary classification model was constructed, and XGBoost was used to construct a classification model of hidden and non-hidden hazard points of landslides. The classification results were compared with the actual situation by calculating the confusion matrix to analyze the accuracy of the model, and the importance of features.

  Results   Hidden danger points of landslides in Xiangxi Autonomous Prefecture are mostly distributed in places where the altitude is 400-600 m and the slope is 3°-30°, the aspect is northwest, and the profile curvature is between -0.6-1.4. From the perspective of the lithology and geological structure of the landslide, the landslides in Xiangxi Autonomous Prefecture are mostly soil landslides, mainly small and medium scales. In terms of geological types, the landslides are mostly distributed in the Cretaceous and Tertiary red layers, and the Triassic Badong Formation red beds and Ordovician marl and marl layers. Results shows that the accuracy rate of identifying hidden danger points of landslides is 91.27%, the sample accuracy rate is 89.75%, and the recall rate is 88.21%. Compared with the random forest algorithm, the accuracy and recall rate of the XGBoost model are higher, indicating that XGBoost can achieve higher accuracy in landslide detection.

  Conclusions   Taking Xiangxi Prefecture, Hunan Province, China as the research area, the spatial distribution characteristics of landslide hidden points are analyzed, and it is found that the landslide hidden points are mostly distributed between 400-600 m, slope 3°-30°, slope to the northwest, curvature -0.6-1.4, low vegetation coverage areas with high soil moisture and obvious human intervention. Based on XGBoost, a landslide hidden point identification model was constructed with an accuracy rate of 91.27%, an accuracy rate and a recall rate of 89.75% and 88.21%, respectively. The accuracy and recall rate of its identification model are higher than the random forest algorithm, indicating that XGBoost is detected in landslide detection.