# Enhancing Predictive Accuracy of Landslide Susceptibility via Machine Learning Optimization
## 4 Results
### Generating Landslide Susceptibility Maps  
For each of the optimized models, the next step of analysis was to calculate the landslide susceptibility index (LSI) for the study area. The range of LSI values is between 0 and 1, which corresponds to the susceptibility of an area to landslides. The available classification methods that could be applied to the produced landslide susceptibility map to divide the LSI value were the equal interval method, quantile method, natural break method, and geometric interval method [69; 70; 71]. After evaluating all the classification methods, the geometric interval method was determined to be the most suitable for this study. This method, along with the other three classification techniques, was applied to all four models to divide the LSI values into five susceptibility levels: very low, low, medium, high, and very high. The classification was conducted using ArcGIS 10.5 software (Figures 6-9). In previous studies, researchers have primarily used the natural break method for landslide susceptibility zoning [72; 73; 74].  
Figure 5: Optimization figures for the CDT model.  
Figure 6: _Cont._Figure 6: Optimization figures for the MLP model.  
The CDT model exhibited a serious blocking phenomenon across the four classification schemes. After partitioning using the quantile and natural interval methods, the resultant maps of the LSI, calculated using the CDT model, differed too much from the results of the remaining three models (Figures 7 and 9). Most of the lower half of the study area was shown to have low landslide susceptibility in the results of the RF, MLP, and NB models, while most of this part was classified as medium landslide susceptibility in the CDT model. Due to these inconsistencies, the natural break and quantile methods were deemed unsuitable for this study. In response to the results obtained by applying the equal interval method, the CDT model, due to its characteristics of splitting to reduce the uncertainty and using the equal interval method, led to the generation of three categories (very high, medium, and very low landslide susceptibility), which demonstrated a clear phenomenon of chunking, as shown in Figure 9. The geometric interval method effectively classified the LSI into five distinct levels across all four models. When compared with the 87 recorded landslide locations, the overall spatial distributions of the landslide susceptibility that were predicted by the four models closely aligned with real-world conditions, demonstrating the reliability and applicability of this classification approach (Figure 10).  
Figure 7: Landslide susceptibility maps based on equal interval method: (**a**) MLP model, (**b**) NB model, (**c**) CDT model, and (**d**) RF model.  
Figure 8: Landslide susceptibility maps based on quantile method: (**a**) MLP model, (**b**) NB model, (**c**) CDT model, and (**d**) RF model.  
Figure 9: Landslide susceptibility maps based on geometric interval method: (**a**) MLP model, (**b**) NB model, (**c**) CDT model, and (**d**) RF model.  
Additionally, landslide density maps were generated for both classification methods to compare their effectiveness. The geometric interval method produced more accurate landslide density distributions across different susceptibility classes compared with the equal interval method. In the equal interval classification, the NB and CDT models exhibited lower landslide densities in the very high and high susceptibility classes, reducing their classification reliability. Furthermore, the equal interval method showed minimal variation in landslide densities across different susceptibility levels, making it less effective for capturing the actual distribution of landslide occurrences (Figure 11).  
Figure 11: Density of different types of landslides.  
Figure 10: Statistical LSI results of different models based on different methods.