# Examination of Landslide Susceptibility Modeling Using Ensemble Learning and Factor Engineering
## 4 Results  
### Factor Importance Analysis  
Figure 6 presents the factor importance rankings. The DEM emerges as the most important factor in the random forest, LightGBM, CatBoost, bagging, and stacking models, with its importance score significantly surpassing that of the other factors, indicating its crucial role in predicting landslide susceptibility. In the random forest model, other important factors, in order of significance, include the distance to the river, lithology, the NDVI, and the distance to the road, all of which demonstrate high importance across  
multiple models. In regard to the XGBoost model, lithology shows exceptionally high importance, far exceeding the other factors, followed by the DEM and land use, which also significantly influence model performance. The LightGBM model similarly highlights the DEM, along with the distance to the river, the distance to the road, and the NDVI, as critical factors. In regard to the CatBoost model, the importance of the DEM is reaffirmed, with lithology and the distance to the river also ranked highly. In regard to the bagging and stacking models, in addition to the DEM, lithology, the distance to the river, and the NDVI consistently exhibit high importance. This consistency across various models underscores the significance of these factors. In summary, the DEM is consistently identified as the most important factor across all the models, emphasizing its critical role in landslide prediction. Lithology and the distance to the river also demonstrate high importance in multiple models, indicating their substantial impact on landslide susceptibility prediction. The bagging and stacking strategies enhance the robustness and comprehensiveness of the factor importance scores by integrating contributions from all the base models.  
Figure 6: Factor importance analysis: (**a**) RF model, (**b**) XGBoost model, (**c**) LightGBM model, (**d**) CatBoost model, (**e**) bagging model, and (**f**) stacking model. PlanC: plan curvature, ProC: profile curvature, DTRiver: distance to the river, DTRoad: distance to the road.  
In regard to the comprehensive analysis of all the influencing factors, the ROC curve highlights the differences in prediction performance among the models (Figure 7). In regard to the random forest model, the DEM is identified as the most critical factor, consistent with its relatively high AUC value of 0.836, underscoring the decisive role of elevation in geological hazard prediction. In contrast, the XGBoost model identifies lithology as the most important factor, which may explain its slightly lower AUC value of 0.820. While lithology is important, its predictive power appears less consistent across the different regions compared to the elevation data. Similarly, both the CatBoost and LightGBM models also highlight the DEM as a significant factor, aligning in regard to their relatively high AUC values of 0.831 and 0.821, respectively. For the bagging and stacking ensemble models, the combined influence of all factors is observed, with both the DEM and lithology emerging as key contributors. This ensemble strategy significantly enhances model robustness and effectively reduces the risk of overfitting, as reflected in their AUC values of 0.835 and 0.839, respectively. Notably, the stacking model achieves the best performance, further demonstrating the advantages of integrating multiple models. This analysis emphasizes the importance of fully considering and utilizing key factors when constructing and optimizing geological hazard prediction models. By doing so, the predictive ability and reliability of these models can be significantly improved.