## 2 Materials
### Data Source and Preparation of Influencing Factors  
#### 2.2.1 Data Source  
(1) Digital Elevation Model (DEM) data: These data were used to extract various topographic factors, including slope, aspect, curvature, topographic wetness index (TWI), flow path length, and flow width. The data were sourced from the Geospatial Data Cloud website ([[http://www.gscloud.cn/](http://www.gscloud.cn/)]([http://www.gscloud.cn/](http://www.gscloud.cn/)) (accessed on 12-July-2024)) with a spatial resolution of 30 m. For example, [PERSON] et al. [25] extracted topographic features such as slope, aspect, and curvature from DEM data and used machine learning models to generate landslide susceptibility maps. Their findings highlight the effectiveness of DEM data in predicting landslide-prone areas.  
(2) Basic geological data: Geological data were used to analyze the engineering geological rock group (EGRG) distribution and slope structure in the study area. The data were sourced from the National 1:200,000 Digital Geological Map (Public Edition) spatial database provided by the Geological Science Data Publishing System ([[http://dcc.ngac.org.cn/cn/page/index](http://dcc.ngac.org.cn/cn/page/index)]([http://dcc.ngac.org.cn/cn/page/index](http://dcc.ngac.org.cn/cn/page/index)) (accessed on 12-July-2024)). For example, [PERSON] et al. [26] investigated the significance of geological data, particularly EGRG, in understanding regional landslide behavior. They used geological map data to analyze landslide distribution and incorporated it into landslide susceptibility modeling.  
(3) Rainfall stations and rainfall data: These data, provided by the China Geological Survey, were used to analyze the relationship between rainfall and landslide occurrence in the study area and to calculate the mean annual rainfall. For example, [PERSON] et al. [27] utilized Kriging interpolation to estimate the annual rainfall distribution in the Xijiang River Basin and found that incorporating spatially distributed rainfall data significantly improved the accuracy of landslide susceptibility assessments. Their study emphasizes the importance of considering rainfall distribution in landslide hazard modeling.  
(4) Land use data and normalized difference vegetation index (NDVI): These data were sourced from the United States Geological Survey (earthexplorer.usgs.gov (accessed on 18-July-2024)) and Landsat-8 imagery. For example, [PERSON] et al. [28] used Landsat-8 imagery to extract NDVI values and assess the role of vegetation in mitigating or exacerbating landslide risks. Similarly, [PERSON] et al. [29] incorporated NDVI and land use data to examine the influence of different land cover types on landslide occurrence and proposed land use planning strategies to reduce landslide risks.  
Figure 1: Distribution of landslide disasters and geographic locations in Badong County.  
(5) Vector data: This included information on administrative boundaries, national water systems, road networks, and other relevant features sourced from the National Geographic Information Resources Catalogue Service System ([[https://www.webmap.cn/](https://www.webmap.cn/)]([https://www.webmap.cn/](https://www.webmap.cn/)) (accessed on 12-July-2024)).