# Examination of Landslide Susceptibility Modeling Using Ensemble Learning and Factor Engineering
## 4 Results
### Landslide Susceptibility Mapping  
The optimized models were applied to the entire dataset of the study area, generating a susceptibility distribution map (Figure 13). The map demonstrates that the ensemble learning models achieve high accuracy in predicting historical landslides in the study area. The random forest model indicates significant portions of the area as low and very low susceptibility, while certain regions with a high density of landslide occurrences are classified as moderate and high susceptibility areas. In contrast, the XGBoost model marks more areas as moderate and high susceptibility, with fewer low susceptibility areas, suggesting a more conservative approach to predicting low-risk regions. Similarly, the LightGBM model predicts many areas as moderate and high susceptibility, with a balanced distribution of susceptibility categories, including some low susceptibility areas. The CatBoost model highlights a large number of moderate and high susceptibility areas, with less emphasis on low susceptibility areas, resembling the predictions of the XGBoost and LightGBM models. The bagging model shows a more balanced distribution of susceptibility levels, with significant low susceptibility areas and a more even distribution of moderate and high susceptibility areas. The stacking model, which combines the predictions of multiple models, exhibits a more detailed distribution of the susceptibility levels, clearly delineating high, moderate, and low susceptibility areas. These results suggest that the stacking model provides the most comprehensive prediction and should be considered the optimal susceptibility map.  
There is a certain amount of consistency among the models in predicting landslide risk across the study area. The northern part of the study area is generally classified as high susceptibility, particularly in the predictions from the XGBoost, LightGBM, and CatBoost models. The central region is also identified as a key area of high susceptibility, especially in regard to the CatBoost and stacking models, where the central area shows clear high susceptibility. In contrast, the random forest model assigns relatively lower susceptibility levels to this region. The southwestern region is classified as moderate susceptibility by most models, particularly the LightGBM and XGBoost models. The eastern part of the study area is commonly marked as low or very low susceptibility across all the models, with the random forest and bagging models showing extensive low-risk areas in the east. The southern region is also a primary area of low susceptibility, particularly in the bagging and stacking models, which widely classify this area as low susceptibility, indicating relative safety and a lower likelihood of landslide occurrences. Overall, the landslide risk distribution in the study area is as follows: the northern and central regions are high  
Figure 12: Susceptibility level of models considering optimal factors for landslide predictions across the whole area.  
susceptibility areas, particularly along the Yangtze River, where landslide occurrences are frequent and require significant attention; the southwestern region is of moderate susceptibility, necessitating appropriate preventive measures; and the eastern and southern regions are low susceptibility areas, which are relatively safe, but still warrant vigilance.