## 4 Results  
### LCFs Selection and Dynamic Factor Change Trend Analysis in the Study Area  
#### 4.1.1 LCFs Selection and Analysis  
In this study, we first chose 12 variables: elevation, aspect, slope, profile curvature, plane curvature, TRI, NDVI, land cover type, surface soil taxonomy, precipitation, lithology, and distance to faults. The \(PCC\) between the LCFs was calculated, with the results depicted in Figure 5. The correlation coefficient between the slope and profile curvature was determined to be 0.725, suggesting a robust association between these two variables. Furthermore, we calculated the \(MI\) between the LCFs and landslides, with the results illustrated in Figure 6. The results indicate that the slope has less information and is less dependent on landslide occurrence. The slope factor was eliminated after careful consideration. After factor elimination, eleven factors, including elevation, aspect, profile curvature, plane curvature, TRI, NDVI, distance to faults, land cover type, surface soil taxonomy, precipitation, and lithology, were retained for the final model.  
Figure 5: The \(PCC\) calculation results of condition factors. (Areas in the red box represent correlations between static factors, areas in the yellow box represent correlations between dynamic factors, and other areas represent correlations between dynamic and static factors.)  
Based on these factor selection results, we further analyzed the interactions between static and dynamic factors to better understand their combined impact on landslide susceptibility. As demonstrated in Figure 5, the red-highlighted areas signify the correlation between static factors, while the yellow-highlighted areas denote the correlation between dynamic factors. The remaining areas indicate the correlation between dynamic factors and static factors. Elevation, slope, and profile curvature exhibit strong correlations, indicating that these topographic static factors collectively influence the geomorphological stability of the slope. Furthermore, precipitation and land cover demonstrate a discernible pattern of correlation, indicating their role as dynamic factors that vary with time and season. It is noteworthy that precipitation exhibits a negative correlation with certain static factors (e.g., plane curvature and distance to faults), suggesting that precipitation-driven landslides may be more prevalent in low-curvature and gently sloping areas due to moisture accumulation. As demonstrated in Figure 6, the _MI_ results illustrate the relative importance of various factors influencing landslide susceptibility. Among the static factors, surface soil type, elevation, and distance to faults exhibit high _MI_ values, underscoring their fundamental role in landslide occurrence. Among the dynamic factors, precipitation and land cover demonstrate significant _MI_ values, emphasizing their direct influence in triggering slope failure. The comparison of _MI_ values between static and dynamic factors suggests that, while static factors primarily determine the baseline susceptibility of an area, dynamic factors such as precipitation act as key triggers, especially in areas that are already susceptible to instability. Therefore, landslide susceptibility is the result of a combination of long-term static topographic features and short-term dynamic environmental conditions.