## 5 Results and Discussion  
### Characteristic Analysis of Landslide Factors  
The results of multicollinearity analysis are depicted in Figure 7. The VIF values for the ten selected landslide influencing factors in this study fall within the range of 1-3. The maximum VIF value (2.189) is observed for the distance to rivers, while the minimum value (1.036) is associated with average annual rainfall. The tolerance values range from 0.4 to 1, with a maximum value of 0.965 and a minimum of 0.457. These results indicate that all ten selected influencing factors satisfy the critical value criteria for VIF and Tolerance. This suggests that they exhibit good independence and do not suffer from collinearity issues. In summary, the ten influencing factors chosen in this study can be confidently utilized in subsequent model training and result evaluation, ensuring the data's validity and accuracy.  
Figure 6: Comparison of generator training results.  
Figure 7: Multicollinearity analysis results of landslide influencing factors.  
Upon verifying the suitability of the chosen factors, the study seeks to acquire an initial insight into the manner in which these factors impact landslides. To this end, the random forest (RF) algorithm is utilized to prioritize the selected influencing factors according to their significance in feature importance. RF is a widely used method in the realms of data mining and feature selection. It reduces model variance by aggregating predictions from multiple decision trees, demonstrating robustness against outliers and noise, handling high-dimensional data effectively, analyzing nonlinear, collinear, and interactive data, and providing variable importance scores during data analysis [50; 51]. In this paper, the importance score of different factors in relation to landslide occurrence is calculated using the out-of-bag (OOB) error rate method within the RF framework. The results are then sorted in descending order of importance. The ranked factors are as follows: distance to roads (0.1716), elevation (0.1664), distance to faults (0.1593), distance to rivers (0.1285), average annual rainfall (0.1129), slope (0.0888), NDVI (Normalized Difference Vegetation Index) (0.0584), aspect (0.0500), lithology (0.0455), and land use (0.0189). Notably, distance to the roads emerges as the most critical factor, underscoring the significant impact of human engineering activities on landslide occurrence. Elevation and the presence of faults create conditions conducive to landslides. Meanwhile, rivers and rainfall contribute to landslides by influencing soil structure, while the other factors exhibit varying degrees of association with landslide occurrence. It is worth noting that the landslide samples mentioned in this research are not solely based on their proximity to human activity areas, but rather obtained through the use of remote sensing interpretation technology from a broader geographic range. Meanwhile, the purpose of the landslide susceptibility assessment is to better prevent landslides that may pose a threat to humans. Landslide samples in remote areas may be relatively fewer, so they have a limited impact on the main purpose of this research. It is precisely based on this comprehensive data collection approach and clear research focus that the results of the ranking of the importance of landslide influencing factors are reasonable.