# Examination of Landslide Susceptibility Modeling Using Ensemble Learning and Factor Engineering
## 4 Results
### Factor Sensitivity Analysis  
Based on the factor importance evaluation shown in Figure 6 for the different models, the least important factors were sequentially removed, and the models were retrained after each removal to assess the impact on the prediction accuracy. The final results are  
Figure 7: ROC curves of models considering all factors: (**a**) RF model, (**b**) XGBoost model, (**c**) LightGBM model, (**d**) CatBoost model, (**e**) bagging model, and (**f**) stacking model.  
presented in Figures 8 and 9. In regard to the random forest model, the AUC value is 0.836 when all the factors are included. Removing the soil type and land use factors slightly reduces the prediction accuracy. However, as additional factors are removed, the AUC value peaks at 0.866 when retaining seven factors (DEM, distance to the river, lithology, NDVI, and distance to the road), representing a 3.5% improvement over the initial model. Further factor removal significantly decreases the AUC value, indicating that the random forest model relies heavily on the combined effect of multiple factors, with key factors playing a critical role in maintaining model performance. In regard to this study area, the optimal number of factors for the random forest model is five. In regard to the XGBoost model, the AUC value is 0.820 with all the factors included. Removing the TWI, the SPI, profile curvature, and the distance to the road improves the AUC value, namely to 0.840, a 2.4% increase, demonstrating a high sensitivity to core factors, while maintaining stable performance with a reduced set of key factors. The LightGBM model initially achieves an AUC value of 0.821. Its performance declines steadily with factor removal, particularly after removing more than six factors. The optimal factor combination, the DEM, the distance to the river, the distance to the road, the NDVI, lithology, and slope, yields the highest AUC value of 0.826, a 0.6% improvement. For the CatBoost model, the AUC value is 0.831 when all the factors are included. Removing plan curvature, profile curvature, the TWI, and the SPI significantly reduces model performance. The optimal factor combination, the DEM, lithology, the distance to the river, the distance to the road, the NDVI, soil type, slope, and land use, achieves an AUC value of 0.842, a 1.3% improvement. The bagging and stacking ensemble models exhibit optimal performance after removing eight factors, retaining the combination of the DEM, lithology, the distance to the river, and the NDVI. The bagging model achieves an AUC value of 0.850, a 2.4% improvement, while the stacking model attains the highest AUC value of 0.876, a 4.4% improvement, demonstrating its superior predictive performance compared to the other models. This series of analyses highlights significant differences in factor selection and optimization strategies among the models in regard to landslide susceptibility prediction. Despite these differences, the DEM consistently emerges as the most critical factor across all the models. These results underscore the importance of the rational selection and optimization of factor combinations to enhance model prediction accuracy.