# Examination of Landslide Susceptibility Modeling Using Ensemble Learning and Factor Engineering
## 5 Discussion  
This study adopted an innovative approach by forgoing traditional collinearity detection methods for factors and, instead, exploring the effectiveness of different ensemble learning models in evaluating and ranking the influencing factors. Specifically, homogeneous ensemble models, namely random forest, XGBoost, LightGBM, and CatBoost, were employed alongside heterogeneous ensemble models, namely bagging and stacking, to comprehensively assess factor importance. The random forest model evaluates factor importance based on Gini impurity or entropy metrics, measuring the improvement in purity at decision tree nodes. XGBoost quantifies the contribution of factor splits to model performance using the \"gain\". LightGBM utilizes Gradient-based One-Side Sampling and Exclusive Feature Bundling techniques, while CatBoost emphasizes the frequency of factor usage across multiple trees and its impact on decision making. Notably, the analysis of factor importance using heterogeneous ensemble models represents a novel strategy. In regard to the bagging and stacking models, the importance scores of the same factor from different models were combined and averaged to form a comprehensive factor importance  
Figure 13: Landslide susceptibility maps: (**a**) RF model, (**b**) XGBoost model, (**c**) LightGBM model, (**d**) CatBoost model, (**e**) bagging model, and (**f**) stacking model. score. The results indicate that the DEM was identified as the most critical factor across all the models, followed by lithology and the distance to the river. This finding aligns with the established understanding in landslide susceptibility research, wherein topographic features have long been recognized as fundamental determinants of slope stability. The DEM, as a comprehensive representation of terrain elevation, slope gradient, and aspect, encapsulates essential information related to gravitational forces, surface runoff, and soil erosion, all of which are key triggering mechanisms for landslides. Previous studies have shown that steeper slopes, as quantified by DEM-derived metrics, increase shear stress on soil masses, while elevation changes can influence hydrological conditions, leading to pore water pressure buildup and subsequent slope failure. Against this backdrop, our study posits a clear hypothesis: in regions characterized by complex topography and variable geological conditions, the DEM should emerge as the dominant factor in landslide susceptibility modeling due to its direct influence on multiple physical processes. The multi-model strategy employed herein, by aggregating factor importance scores from random forest, XGBoost, and other ensemble algorithms, not only enhanced the robustness of the predictions, but also reduced the risk of overfitting, thereby improving the accuracy and reliability of the results. This convergence in the findings across diverse models strengthens the validity of our hypothesis, as it demonstrates that the DEM's influence is consistently recognized regardless of the underlying algorithmic approach used. Furthermore, a novel method was employed to progressively remove factors deemed unimportant based on their assessed importance across different models, followed by retraining the models to explore the impact of these operations on prediction accuracy. The experimental results revealed significant performance differences among the models after the removal of certain factors, primarily due to the varying mechanisms and dependencies each model has in regard to the different factors. For instance, the random forest model relies on the collective intelligence of multiple decision trees, and the absence of certain key factors leads to a noticeable drop in performance. The XGBoost model demonstrates high sensitivity to core factors, iteratively correcting errors from previous steps, and, thus, heavily depends on the correctness of crucial factors.