# Examination of Landslide Susceptibility Modeling Using Ensemble Learning and Factor Engineering
## 5 Discussion
The performance comparison in Table 2 demonstrates that our optimized stacking ensemble (AUC = 0.876) outperforms not only traditional machine learning methods (SVM = 0.813, logistic regression = 0.792) [3], but also deep learning approaches (CNN = 0.845, DNN = 0.832) [19; 20]. Specifically, the stacking model achieves a 3.1% higher AUC than CNN and a 4.4% improvement over DNN, highlighting its effectiveness in regard to landslide susceptibility prediction. The optimized models were applied to the entire study area to create a landslide susceptibility distribution map. The results indicated that the stacking model, which integrates predictions from multiple base models, provided the most detailed distribution of susceptibility zones, clearly distinguishing between high, medium, and low susceptibility areas, with excellent performance. In the landslide susceptibility prediction for the study area, the models exhibited high consistency. Specifically, the northern region, characterized by steep terrain and concentrated rainfall, was classified as a high susceptibility area. The central region, influenced by frequent geological activities and human interventions, was also identified as a high susceptibility zone. The southwestern region, with its complex terrain and climatic conditions, was assessed as having moderate susceptibility. In contrast, the eastern and southern regions, with relatively stable geological conditions, were classified as low susceptibility areas, indicating a lower risk of landslides. From a regional distribution perspective, the northern and central areas, classified as high susceptibility zones, require focused monitoring and preventive measures. The southwestern region, as a \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Optimized Stacking** & **SVM** & **Logistic Regression** & **CNN** & **DNN** \\ \hline AUC & 0.876 & 0.813 & 0.792 & 0.845 & 0.832 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of different models. moderate susceptibility zone, should implement appropriate protective measures to mitigate potential risks. The eastern and southern regions, classified as low susceptibility zones, are relatively safe, but should remain vigilant against potential hazards. The landslide susceptibility modeling strategy developed in this study not only improved the accuracy of landslide risk predictions, but also facilitated more effective landslide disaster prevention and mitigation efforts. This approach ensures the safety of residents and infrastructure within the region, while providing robust scientific support for the detailed delineation of landslide susceptibility zones and risk management. Despite the promising results of this study, several limitations warrant consideration. Firstly, while a variety of ensemble learning models were employed, the selection of the factors was primarily based on the available geological and environmental data, which may not encompass all the relevant variables influencing landslide susceptibility. Consequently, if the model were applied to another region or country with different geological and climatic conditions, there is a significant risk that the predictions may be inaccurate or unreliable. This limitation arises because the model's performance is heavily dependent on the quality and relevance of the input data; factors that are critical in one region may not hold the same importance in another. Future research should aim to incorporate additional data sources, such as socio-economic factors and data from real-time monitoring systems, to enrich the model inputs and enhance the predictive accuracy. Secondly, the models were evaluated within a specific geographical context, which may limit the generalizability of the findings to other regions. Therefore, it is essential to validate the developed models in diverse settings to assess their robustness and adaptability.