## 5 Discussion
### Feature Importance Analysis  
To gain a comprehensive and accurate understanding of the model's internal operations, we calculated the SHAP values of the model's validation set [46; 47]. The rankings of feature importance and SHAP values after interpreting the model are shown in Figure 21. From the results, we can see the significant impact of factors such as road distance, surface roughness, aspect, land use type, plane curvature, NDVI, and annual precipitation. Referring to Figure 5, it is evident that roughness and slope have higher impacts among terrain factors, with roughness carrying greater weight, while the other terrain factors have relatively lower impacts. On the other hand, factors like distance to rivers, profile curvature, lithology, distance to faults, and TWI have relatively smaller impacts on the model. These rankings of feature importance align with the interpretations from optical remote sensing and on-site observations. In this model, terrain factors and the influence of human activities have higher importance. Land use type and distribution factors have moderate importance, while meteorological hydrology and engineering geology factors have relatively lower importance in the model.  
Given the minor impact of lower-ranked factors on the model, this study selectively examines the primary seven influential factors for analytical simplicity. As depicted in Figure 21, there exists a negative correlation between road proximity and the projected susceptibility to landslides, where closer proximity to roads corresponds to higher susceptibility, and increased distance results in lower susceptibility. As the study area lies within a hilly landscape in the central mountains, construction activities such as roads and tunnels are undertaken to enhance accessibility. However, these projects often involve slope cutting, internal excavation, and alteration of the original stress distribution within the bedrock. The presence of weak features like structural surfaces within slope rock formations could significantly compromise slope stability [48], thereby escalating the vulnerability to landslides and other geological hazards.  
Elevation, roughness, and gradient factors share similarities with the terrain influence factor. Topographical attributes are prerequisites for the occurrence of landslide disasters [49]. As the slope gradient increases, the extent of the stress unloading zone on the slope surface widens, resulting in elevated concentrated stress at the slope toe, thus raising the probability of landslides. Steeper slopes experience higher internal stress within the slope mass, making them more prone to deformation and the formation of landslides.  
Figure 20: IVCI model ROC curve. An AUC value exceeding 0.9 signifies exceptional model performance, demonstrating outstanding classification capability within the model.  
Referring to Figure 22, the patterns of slope and roughness demonstrate resemblance; higher predictive values primarily cluster within the 22\({}^{\circ}\) slope range, while roughness predominantly centers around 1.44, indicating frequent minor-scale landslides in the vicinity of residences. Land use classification mirrors the impact of human activities, with extensive human interventions observed in cultivated and anthropogenically altered land surfaces. In contrast, woodland and shrubland regions experience fewer human activities and thereby showcase relatively higher local stability.  
Figure 21: Map of the results of the feature importance analysis. (**a**) Feature importance ranking; (**b**) SHAP summary plot of the test dataset.