# Examination of Landslide Susceptibility Modeling Using Ensemble Learning and Factor Engineering
## 6 Conclusions  
This study aimed to enhance landslide susceptibility modeling by integrating various machine learning models, including random forest, XGBoost, LightGBM, CatBoost, bagging, and stacking. The focus was on evaluating and ranking the importance of different influencing factors and employing advanced ensemble learning strategies to improve prediction accuracy and robustness. The methodology involved comprehensive factor importance analysis, stepwise factor elimination, and optimization of model performance. Homogeneous ensemble models (random forest, XGBoost, LightGBM, and CatBoost) and heterogeneous ensemble models (bagging and stacking) were utilized to assess and integrate the importance of each factor. The models were trained and tested using a dataset comprising factors such as the DEM, lithology, the distance to the river, the NDVI, and slope. The key findings indicate that the DEM consistently emerged as the most critical factor across all the models, underscoring its pivotal role in predicting landslide susceptibility. The stacking model achieved the highest AUC value of 0.876, representing a 4.4% improvement over the other models and demonstrating superior predictive performance. The factor engineering process significantly enhanced model accuracy. For instance, the proportion of areas classified as highly susceptible by the stacking model increased to 42.54%, while the proportion classified as very low susceptibility by the XGBoost model rose from 12.96% to 13.57%. These improvements highlight the effectiveness of factor optimization in enhancing model performance. The optimized models were applied to create a detailed landslide susceptibility distribution map of the entire study area. The results indicated that the stacking model provided the most detailed distribution, clearly distinguishing between high, medium, and low susceptibility areas. The northern and central regions were classified as high susceptibility areas due to their steep terrain and concentrated rainfall, while the eastern and southern regions exhibited low susceptibility, indicating a lower landslide risk. These findings demonstrate the utility of ensemble models in producing robust and detailed landslide susceptibility maps. In conclusion, the innovative approach of integrating multiple models and employing meticulous factor engineering strategies significantly improved the accuracy and robustness of landslide susceptibility predictions. The findings offer valuable insights for effective landslide disaster prevention and mitigation efforts, ensuring the safety of residents and infrastructure in susceptible regions. This study highlights the potential of ensemble learning techniques to enhance the reliability of landslide susceptibility assessments and provides a scientific basis for informed decision making in regard to landslide risk management. Writing--original draft preparation: L.Z. ; writing--review and editing: T.Z. ; methodology: T.Z., L.W. ; investigation: B.J., S.Y., D.H., W.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (U22A20600), the National Natural Science Foundation of China (51708068), the Chongqing Postgraduate Mentoring Team Construction Project (JDDSTD2022009), the Chongqing Construction Science and Technology Plan Project (Chengkezi 2023 No. 7-7), the Chongqing Construction Science and Technology Plan Project (Chengkezi 2022 No. 1-12), the State Grid Chongqing Project (2025-51#), the Chongqing Technology Innovation and Application Development Special Project (CSTB2022 TIADGPX0046), the Chongqing Talent Program Innovation and Entrepreneurship Demonstration Team (CQYC-201903204), and the China Postdoctoral Science Foundation (2024 MD764034). Not applicable to studies that do not involve humans or animals. Not applicable to studies that do not involve humans. Not applicable to studies that do not involve humans or animals. * [PERSON] et al.