# Landslide Susceptibility Modeling Using a Deep Random Neural Network
## 3 Study Area and Data Used
### Landslide Conditioning Factors  
The selection of landslide conditioning factors is important in LSM. This study mainly consults the work of [PERSON] et al. (2020), who performed a detailed analysis of landslide  
Figure 4: (**a**) Locations of landslide; (**b**) Locations of non-landslide.  
influencing factors in the study area [27]. The only difference is that the plan curvature was replaced with the topographic wetness index (TWI) because the plan curvature makes little contribution to landslide prediction. Accordingly, a total of 12 factors which are associated with landslide occurrence were considered for modeling landslide susceptibility in this study. These factors include elevation, slope angle, slope aspect, profile curvature, TWI, ERG, land use, distance to roads, distance to rivers, distance to faults, rainfall, and normalized difference vegetation index (NDVI). The selected factors comprehensively reflect the topographical, geological, hydrological, and anthropogenic features of the study area.  
The digital elevation model (DEM) data were first prepared using a digital contour map at a scale of 1:10000. The DEM was converted into the raster format with a resolution of 30 m. The terrain factors, including the elevation, slope angle, slope aspect, and profile curvature, were extracted from the DEM. TWI is also a commonly used factor that indicates the influence of terrain on soil runoff processes, which is calculated as follows:  
\[\text{TWI}=\ln\left(\frac{\alpha}{\tan\beta}\right) \tag{9}\]  
where \(\alpha\) is the cumulative catchment area, and \(\beta\) is the slope angle.  
The distance to roads, distance to rivers, and land use were collected according to the Third Detailed Land Investigation Nationwide (China) at the county scale. The distance to roads and the distance to rivers range from 0 m to 9993 m and 0 m to 2567 m, respectively. The land use map was reclassified into seven main types: residential area, forest, grassland, farmland, bare land, engineering land, and other lands.  
Geological factors can never be neglected in analyzing landslide occurrence probability [75]. The ERG and distance to faults are extracted from a 1:100,000-scale geological map. The ERG of the study area includes seven groups: the group of layered, massive hard metamorphic rock; the group of layered, flaky soft mudstone, shale, and sandstone; the group of layered karst medium hard carbonate rock; the group of layered hard sandy mudstone; the group of massive, vein hard extrusive rock; the group of massive hard intrusive rock; the group of loose and semi-cemented rock dominated by gravel and sand. The range of the distance to faults is 0-12,192 m.  
The NDVI is an important indicator that reflects the relationship between landslides and vegetation [51]. The NDVI was calculated based on the 30 m \(\times\) 30 m Landsat 8 OLI remote sensing image from the USGS website ([[https://earthquake.usgs.gov/](https://earthquake.usgs.gov/)]([https://earthquake.usgs.gov/](https://earthquake.usgs.gov/))). The remote sensing image was acquired on 7 March 2018. The calculation of the NDVI is related to the red band and infrared band:  
\[\text{NDVI}=\frac{Band_{IR}-Band_{B}}{Band_{IR}+Band_{IR}} \tag{10}\]  
where \(Band_{IR}\) and \(Band_{R}\) represent the red band and infrared band, respectively.  
Rainfall is a critical factor contributing to slope movement. We collected the annual average rainfall data of all villages from the Yunnan Digital Village Website ([[http://www.ynszxc.net](http://www.ynszxc.net)]([http://www.ynszxc.net](http://www.ynszxc.net))). The data was collected on 19 September 2018. Then, we produced an annual rainfall map using the Kriging interpolation method.  
All vector layers were transformed into 30 m \(\times\) 30 m raster data in order to unify the data format (Figure 5). To make the data structure suitable for the DRNN, the stacking factors were reshaped to 1D sequence factors in a tensor format when input to the DRNN.  
Figure 5: _Cont._