# Examination of Landslide Susceptibility Modeling Using Ensemble Learning and Factor Engineering
## 5 Discussion
LightGBM, utilizing Gradient-based One-Side Sampling and Exclusive Feature Bundling techniques, is highly sensitive to the total gain of certain factors when optimizing high-dimensional data. CatBoost evaluates the frequency of factor usage across decision trees and excels in regard to handling categorical factors, but the removal of key factors also significantly impacts its performance. These findings collectively highlight the importance of meticulous factor selection and optimization for enhancing predictive performance and stability across different models. Notably, the integration of predictions from multiple base models using bagging and stacking strategies not only bolstered the robustness of the overall model, but also improved the predictive accuracy. After factor removal, these ensemble models exhibited greater resilience to fluctuations in input data and demonstrated high robustness under varying conditions. However, the post-factor removal performance varied among the ensemble models, reflecting the diversity in regard to the factor handling strategies and methodologies inherent to each base model. This variability underscores the necessity of a tailored approach to factor selection and model training, as different models may react differently to changes in the input factors. Such differences ultimately influence the overall effectiveness of landslide susceptibility assessments, emphasizing the importance of understanding model-specific behaviors when optimizing factor combinations. In the further research conducted, the performance of different models utilizing specific combinations of factors based on the training and testing datasets was analyzed, with a detailed examination of the landslide susceptibility prediction results. This comprehensive analysis revealed significant performance disparities among the models when predictingareas of high susceptibility versus those of extremely low susceptibility. Notably, the stacking model excelled in identifying high susceptibility areas, underscoring its exceptional ability to recognize regions at elevated risk of landslides. Conversely, the XGBoost model demonstrated superior effectiveness in pinpointing extremely low susceptibility areas, highlighting its precision in accurately locating low susceptibility regions. By optimizing the factor combination techniques for each model, improvements in prediction accuracy were observed for both high and extremely low susceptibility areas across all the models. This finding suggests that meticulous factor engineering not only effectively reduces prediction uncertainty, but also significantly enhances overall model accuracy. For instance, after factor optimization, the stacking model exhibited a remarkable increase in the proportion of correctly identified high susceptibility areas, while XGBoost's accuracy in identifying extremely low susceptibility areas also showed a notable improvement. Moreover, the analysis revealed that each model exhibited unique sensitivities and adaptive capabilities in predicting varying susceptibility levels, primarily driven by their distinct internal mechanisms and factor handling methodologies. The bagging and stacking strategies, which integrate predictions from multiple base models, demonstrated heightened robustness, particularly outperforming individual models in predicting both high and low susceptibility levels. These strategies highlighted their ability to balance the strengths of different models, resulting in improved prediction stability and accuracy. This systematic investigation of model performance illuminated the nuanced differences in how various models respond to changes in the input factors. It underscores the critical importance of meticulous factor engineering and multi-model integration strategies in enhancing prediction accuracy and stability. By leveraging the complementary strengths of individual models, ensemble approaches such as bagging and stacking provide a robust framework for improving the reliability of landslide susceptibility predictions.