## 3 Results and Discussion
### Feature Contribution Analysis Using SHAP Values  
To better understand the non-linear interactions between key landslide conditioning factors, SHAP (SHapley Additive exPlanations) interaction plots were generated. SHAP is a game-theoretic approach that assigns each feature an importance value for a particular prediction. The SHAP value quantifies the impact of an individual feature on the model's output: positive SHAP values indicate an increase in the predicted probability of landslide occurrence, while negative values suggest a decrease. In the graphical outputs, features are plotted along the Y-axis, and their SHAP values are shown on the X-axis. The color gradient represents feature values, with blue indicating lower values and red indicating higher values.  
The SHAP beeswarm summary plot (Figure 16a) and mean SHAP value plot (Figure 16b) reveal the relative importance and directional influence of landslide conditioning factors. Among all features, elevation exhibited the highest mean SHAP value (+1.55), indicating its dominant role in increasing landslide susceptibility, particularly in high-altitude zones. Other prominent features included distance to fault (+0.92), peak ground acceleration (PGA) (+0.78), slope (+0.68), and distance to stream (+0.64). These results underscore the significant contribution of topographic and seismic factors in co-seismic landslide initiation.  
The beeswarm plot further illustrates how high values of elevation, slope steepness, and ground acceleration contribute positively to landslide probability, as indicated by the clustering of red-colored points with high SHAP values. In contrast, features such as NDVI, distance to road, and aspect showed lower SHAP values, reflecting more modest but still relevant roles in the predictive framework. Notably, the distance to stream displayed both positive and negative SHAP values across its range, indicating a non-linear relationship with landslide susceptibility. This highlights the advantage of SHAP in capturing complex feature interactions that are often overlooked by traditional linear feature importance metrics. The distance to fault and PGA reveal heightened feature importance, which are pivotal conditioning factors for the co-seismic landslides.  
Figure 15: Model performance comparison with and without rainfall.  
Further insights into feature interdependencies were obtained through SHAP interaction plots (Figure 17). The elevation \(\times\) distance to fault interaction (Figure 17a) revealed that landslide probability was highest at elevated terrains in close proximity to fault lines. This spatial combination likely reflects the presence of structurally weakened lithologies and fractured bedrock in tectonically active zones. Similarly, the PGA \(\times\) slope interaction (Figure 17b) indicated that steep slopes subjected to high seismic acceleration showed markedly elevated susceptibility. These compound effects suggest that ground shaking and steep terrain amplify landslide hazards synergistically, affirming the geomechanical relevance of multi-variable interactions in co-seismic settings.  
Overall, the SHAP-based analysis enhances both the interpretability and geoscientific robustness of the machine learning models. By elucidating how different factors, both alone and in interaction, contribute to landslide probability, this approach supports more transparent and informed susceptibility mapping. It also emphasizes the importance of considering complex, non-linear relationships when modeling natural hazard phenomena, such as earthquake-induced landslides.  
Figure 16: SHAP analysis. (**a**) Beeswarm plot showing feature impacts on landslide prediction. (**b**) Bar chart of mean SHAP values.  
Figure 17: SHAP interaction plots illustrating the compound effects of (**a**) elevation \(\times\) distance to fault and (**b**) peak ground acceleration (PGA) \(\times\) slope on landslide susceptibility predictions.