## 5 Discussion  
### The Dominant Factors of Landslides in the Study Area  
The selection of landslide impact factors is one of the key steps of landslide susceptibility assessments. Including uncorrelated factors commonly increases model uncertainty [83]. Various methods have been used to select appropriate landslide influencing factors, but there are no definite rules or universal methods for how to select the best combination of factors [52]. As a statistical model, GeoDetector can make full use of the spatial information included in the data to calculate the degree of explanation of the independent variables relative to the dependent variables. Several current studies [44; 45; 52] set the \(q\) value threshold based on empirical knowledge, to eliminate factors below the threshold, which are highly subjective approaches. In addition, adapting GeoDetector to the used landslide susceptibility evaluation model should also be considered. To address these problems, the GeoDetector method was combined with the concept of RFE to construct a new mixed factor screening method that can be applied to statistical models. A previous study [54] combined these two methods, applied them to the random forest model, and achieved good results. On this basis, the current study applies a combination of these two methods  
Figure 11: ROC curves of different models on the testing dataset. to the traditional bivariate statistical model (SI). The RFE method could be improved to more effectively combine the GeoDetector with the SI model. The initial factor set contains 20 factors. Through the GeoDetector preliminary screening, two factors (i.e., plan curvature and profile curvature) that fail to pass the significance test were eliminated. Then, using the hybrid method of GeoDetector and RFE, six factors that negatively impacted the model were eliminated, and 12 factors were accordingly retained. By comparing the AUC of the ROC curves of the original factor set and the optimized factor set on the model, the predictive ability of the model using the retained 12 factors (0.936) was found to be higher than that using 20 factors (0.936) (see Figure 11). The number of factors was decreased and the performance of the model was improved, which proves the effectiveness of factor screening. GeoDetector results (see Figure 5) show that among the 12 factors that were finally selected, distance from roads, distance from main rivers, and altitude are the three factors with the strongest effect on landslide occurrence. Historical landslides (see Figure 1) are generally distributed along both sides of roads and rivers, which is consistent with the results of GeoDetector showing that these two factors largely control the distribution of landslides. In addition, the SI values in Table 2 and the regression results in Figure 8a,b show that with increasing distance from main rivers and roads, the SI weight value generally tends to decrease, and the probability of landslide occurrence also gradually decreases, which is consistent with the results of most studies [30; 84]. Furthermore, another conclusion of this study is that the impact of rivers at different scales on landslide occurrence is inconsistent. The hydrological network in the study area was classified into main rivers and streams according to their level of tributaries. Figure 5 shows that the distance from streams has little correlation to landslide occurrence (\(q<0.05\)), while the distance from main rivers has a higher \(q\) value (\(q=0.626\)), which is largely due to the different scour and erosion capacities of rivers of different scales. Therefore, future research should consider this difference. The importance of altitude (\(q=0.555\)) ranks after the distance from main rivers. An altitude ranges between 1000-1400 m (SI = 1.246) is most conducive to the occurrence of landslides, while in high-altitude areas, the probability of landslides is very low. Two studies have reached the same conclusion [28; 53].