# Examination of Landslide Susceptibility Modeling Using Ensemble Learning and Factor Engineering
## 2 Geological Background
### Landslide Inventory and Influencing Factors
The plan curvature and profile curvature describe terrain concavity/convexity, influencing water flow convergence, sediment distribution, and surface erosion processes, all critical to landslide initiation. The Topographic Wetness Index, derived from DEM data using ArcGIS 10.6, quantifies localized soil moisture; elevated TWI values suggest higher saturation and, thus, greater landslide likelihood. The Stream Power Index reflects runoff erosive potential, indicating how precipitation and overland flows contribute to slope destabilization. The lithological data are derived from a 1:50,000  
Figure 3: Influencing factors: (**a**) DEM, (**b**) slope, (**c**) plan curvature, (**d**) profile curvature, (**e**) SPI, (**f**) TPI, (**g**) lithology, (**h**) soil type, (**i**) distance to river, (**j**) distance to road, (**k**) land use, and (**f**) NDVI. scale geological map, provided by local natural resource departments, in vector format. Lithology forms the geological foundation for landslide occurrences, as the mechanical properties and weathering characteristics of different lithological materials significantly affect slope stability. For instance, clay-rich rocks are more susceptible to landslides when saturated, while sandstone and limestone can also be vulnerable under heavy rainfall or seismic activity. The soil type data are obtained from the 1:1,000,000 scale Soil Map of China, compiled by the Nanjing Institute of Soil Research, Chinese Academy of Sciences, in vector format. The soil type directly influences landslide occurrences, with variations in permeability, water retention, and shear strength affecting soil stability. The distances to the road and the river are calculated using the Euclidean distance tool in ArcGIS and formatted as raster data. Road construction can disrupt the natural stability of the surface, increasing the risk of landslides. River erosion, on the other hand, can weaken slope support and trigger landslides. By calculating these distances, we can assess the potential impact of human activities and natural water systems on landslides. The Normalized Difference Vegetation Index (NDVI) is derived from Landsat 8 TM imagery, processed using ENVI 5.3 software, formatted as raster data. The NDVI reflects the surface vegetation cover, which plays a vital role in stabilizing soil, reducing rainfall-induced soil erosion, and surface runoff, thus lowering the risk of landslides. The land use data are sourced from the Land Use Map, based on 2020 Landsat 8 imagery, produced by the Resource and Environmental Science Data Center (RESDC) of the Chinese Academy of Sciences, formatted as raster data. Land use reflects different patterns of human activity and land use, each influencing landslide risk differently. For instance, farmland, built-up land, and forest land have varying landslide risks. Agricultural and built-up land may see reduced surface stability due to human activities, whereas forest land, with better vegetation cover, generally has lower landslide risk.