## 4 Modeling Process and Results
### Correlation between Selected Factors and Landslide  
Through factor screening, 12 landslide influencing factors were retained. Among them, the distance from roads is the most important factor (\(q\) = 0.701), and its SI value is the highest (1.478) when it is 200-400 m, indicating that it is most favorable for the occurrence of landslides in this class. As shown by the GPR regression result (see Figure 8a), the greater the distance from roads, the lower the probability of landslide occurrence. The distance from main rivers (\(q\) = 0.626) ranked second in importance with the largest SI value (1.437) at 0-200 m. Similar to distance from roads, the factor weight is approximately inversely proportional to the distance (see Figure 8b). As the third most important factor, altitude (\(q\) = 0.555) is most favorable for the occurrence of landslides at 1000-1400 m (SI = 1.246), and no landslides occurred in areas above 2600 m. The importance of distance from hydropower stations is second only to that of altitude (\(q\) = 0.36) as a human engineering factor in this study. When it is 0-500 m, the SI value is the largest (1.875), and the larger the distance, the smaller the SI value (see Figure 8d). Aspect (\(q\) = 0.099), slope (\(q\) = 0.08), degree of relief (\(q\) = 0.059), and TWI (\(q\) = 0.031) are four topographic factors derived from the digital elevation model, and all have a relatively weak influence on landslide occurrence (\(q\) < 0.1). For Aspect, the probability of landslide is highest in the southeastern direction  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Factors** & **RMSE** \\ \hline Altitude & \(3.463\times 10^{-4}\) \\ Degree of relief & \(1.296\times 10^{-4}\) \\ Slope & \(1.606\times 10^{-4}\) \\ Aspect & \(1.356\times 10^{-4}\) \\ Distance from main rivers & \(6.249\times 10^{-4}\) \\ Distance from roads & \(6.225\times 10^{-2}\) \\ Distance from hydropower stations & \(1.361\times 10^{-2}\) \\ TWI & \(1.158\times 10^{-4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Root Mean Squared Error (RMSE) of GPR regression results.  
Figure 7: The algorithm for optimizing the SI model by Gaussian process regression.  
(SI = 0.616). With an increasing slope (see Table 2 and Figure 8f), the probability of landslide occurrence gradually increases. When the degrees of relief and TWI are 30.94-43.98 m and 5.67-7.18, SI values are the largest at 0.353 and 0.554, respectively. In addition, for geological factors, the two discrete factors stratigraphy (\(q\) = 0.117) and lithology (\(q\) = 0.019) were retained. For stratigraphy, results show that in Devonian units, landslides are most likely to occur (SI = 0.905), while for lithology, the probability of landslides is highest in hard rock (SI = 0.315). Finally, for environmental factors, in addition to the distance from main rivers, the two factors of soil erosion intensity (0.272) and land cover (0.111) were retained. For soil erosion intensity, hydraulic erosion level 14 (SI = 1.546) is most likely to cause landslides. For land covers, except for water bodies and artificial surfaces, forestland (SI = \(-\)0.103) is not conducive to the occurrence of landslides, no landslides have occurred on grassland, and farmland (SI = 1.246) is relatively more favorable for the occurrence of landslides.  
Figure 8: Results of weight regression using Gaussian process regression algorithm: (**a**) distance from roads; (**b**) distance from main rivers; (**c**) altitude; (**d**) distance from hydropower stations; (**e**) aspect; (**f**) slope; (**g**) degree of relief; (**h**) TWI.