## 3 Materials and Methods
### Landslide Susceptibility Assessment  
#### 3.3.1 Sampling the Training and Validation Dataset  
This study used the common 1:1 landslide/non-landslide sampling strategy [37], while the non-landslide points were sampled using two different methods (Figure 6): Group 1: sample randomly within the AOI; Group 2: sample within the interpolated section of the AOI and the Low and Medium hazard level area of The Global Landslide Hazard Map (GLHM) to avoid accidentally including points located in actual landslide-prone areas into the non-landslide dataset. The application of Group 2 (GLHM) has resulted in a notable reduction in the concentration of elevation within the dataset. Specifically, the median elevation for Group 2 is 2619 m, compared to 2756 m for Group 1 (Random) (Figure 7).  
Figure 5: _Cont._  
Figure 5: _Cont_.  
Figure 5: Contributing factors for landslide susceptibility modeling represented as raster images with a spatial resolution of 30 \(\times\) 30 m images: (**a**) aspect; (**b**) elevation; (**c**) land cover; (**d**) distance to roads; (**e**) lithology; (**f**) NDVI; (**g**) plain curvature; (**h**) profile curvature; (**i**) roughness; (**j**) slope; (**k**) snowmelt; (**l**) STI; (**m**) TWI; (**n**) rainfall; (**o**) distance to rivers; (**p**) distance to faults.  
The global landslide hazard map is a quantitative landslide hazard map for the whole world produced by Arup under the joint request of the World Bank and the Global Facility for Disaster Reduction and Recovery (GFDRR). The dataset comprises gridded maps of estimated annual frequency of significant landslides per square kilometer, categorized as follows: Very low: \(<\)0.0001 per square kilometer; Low: 0.0001-0.001; Medium: 0.001-0.01; High: \(>\)0.01. This project was conducted based on global-level datasets to show spatial and temporal variation of landslide occurrence internationally, however lacking accuracy on a regional level. Only three events in Xinjiang are listed in the global landslide hazard map inventory, to the extent that the map reflects the homogeneous features of landslides reported worldwide.  
The values of contributing factors for all landslide and non-landslide points were extracted from their respective raster grids to form the dataset. The dataset--comprising 102 landslide points (assigned a value of 1) and 102 non-landslide points (assigned 0)--was then randomly split into training (70%) and validation (30%) groups for the modeling procedure. Prior to training, the input features were normalized using L2 normalization  
Figure 6: The landslide inventory map displays both landslide points and non-landslide points. (On the left, non-landslide points are randomly sampled from the area of interest. On the right, non-landslide points are specifically selected from within the Low and Medium hazard levels as defined by The Global Landslide Hazard Map.)  
Figure 7: Box plot comparing elevation distributions between Group 1 (Random method) and Group 2 (GLHM), two different methods for sampling non-landslide points.  
to ensure consistent scaling across variables, thereby reducing the risk of overfitting and enhancing the stability and efficiency of the learning process.