## 4 Modeling Process and Results
### Construction of the GD-SI Model  
#### 4.2.1 GeoDetector Result  
GeoDetector analysis was performed using both the spatially superimposed factor data and the landslide training dataset. In this study, landslide influencing factors are independent variables, and the classification is consistent with Table 1, while the dependent variable is the occurrence of a landslide (in which case a value of 1 is assigned) or no occurrence of a landslide (in which case a value of 0 is assigned), which is a binary variable. Because GeoDetector requires negative samples, random sampling was performed to produce the same amount of non-landslide samples. To reduce contingency and make the analysis results more reliable, 10 times random sampling of non-landslide samples were conducted to obtain the 10 times GeoDetector results. The analysis result is determined by the average \(q\) value and \(p\) value. The factor detector results are shown in Figure 5. The \(q\) value is the index of the factor's explanatory power for landslides, and the \(p\) value represents the statistical significance. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Factor**} & \multirow{2}{*}{**Class**} & \multirow{2}{*}{
\begin{tabular}{c} **No. of Pixels** \\ **in Domain** \\ \end{tabular} } & \multicolumn{2}{c}{**Percentage of**} & \multicolumn{2}{c}{**No. of**} & \multicolumn{2}{c}{**Percentage of**} \\  & & **Pixels in** & **Landsslides** & **Landsslides in** & **SI Weight** \\  & & & **Domain (\%)** & **in Domain** & **Domain (\%)** & \\ \hline \multirow{8}{*}{Soil erosion intensity} & \(11\) & 726,211 & 70.89\% & 173 & 50.14\% & \(-\)0.346 \\  & \(12\) & 70,452 & 6.88\% & 77 & 22.32\% & 1.177 \\  & \(13\) & 27,335 & 2.67\% & 31 & 8.99\% & 1.214 \\  & \(14\) & 20,886 & 2.04\% & 33 & 9.57\% & 1.546 \\  & \(15\) & 17,127 & 1.67\% & 7 & 2.03\% & 0.194 \\  & \(16\) & 19,169 & 1.87\% & 24 & 6.96\% & 1.313 \\  & \(31\) & 113,698 & 11.10\% & 0 & 0.00\% & \(-\)3.500 \\  & \(32\) & 1829 & 0.18\% & 0 & 0.00\% & \(-\)3.500 \\  & \(33\) & 5632 & 0.55\% & 0 & 0.00\% & \(-\)3.500 \\  & \(34\) & 19,795 & 1.93\% & 0 & 0.00\% & \(-\)3.500 \\  & \(35\) & 2321 & 0.23\% & 0 & 0.00\% & \(-\)3.500 \\ \hline \multirow{8}{*}{Distance from roads (m)} & 0–200 & 120,310 & 11.74\% & 136 & 39.42\% & 1.211 \\  & 200–400 & 74,508 & 7.27\% & 110 & 31.88\% & 1.478 \\  & 400–600 & 60,263 & 5.88\% & 40 & 11.59\% & 0.679 \\  & 600–800 & 52,768 & 5.15\% & 28 & 8.12\% & 0.455 \\  & 800–1000 & 46,377 & 4.53\% & 15 & 4.35\% & \(-\)0.040 \\  & 1000–1200 & 41,758 & 4.08\% & 10 & 2.90\% & \(-\)0.341 \\  & 1200–1400 & 38,641 & 3.77\% & 2 & 0.58\% & \(-\)1.873 \\  & 1400–1600 & 35,999 & 3.51\% & 4 & 1.16\% & \(-\)1.109 \\  & \(>\)1600 & 553,831 & 54.06\% & 0 & 0.00\% & \(-\)3.500 \\ \hline \multirow{8}{*}{Residential kernel density} & 0–1.07 & 586,432 & 57.24\% & 69 & 20.00\% & \(-\)1.052 \\  & 1.07–3.07 & 132,263 & 12.91\% & 60 & 17.39\% & 0.298 \\  & 3.07–5.37 & 125,950 & 12.29\% & 59 & 17.10\% & 0.33 \\  & 5.37–8.10 & 106,213 & 10.37\% & 111 & 32.17\% & 1.132 \\  & 8.10–12.34 & 50,935 & 4.97\% & 37 & 10.72\% & 0.769 \\  & \(>\)12.34 & 22,662 & 2.21\% & 9 & 2.61\% & 0.165 \\ \hline \multirow{8}{*}{Distance from hydropower stations (m)} & 0–500 & 21,405 & 2.09\% & 47 & 13.62\% & 1.875 \\  & 500–1000 & 49,830 & 4.86\% & 68 & 19.71\% & 1.399 \\ \cline{1-1}  & 1000–1500 & 64,863 & 6.33\% & 38 & 11.01\% & 0.554 \\ \cline{1-1}  & 1500–2000 & 78,032 & 7.62\% & 61 & 17.68\% & 0.842 \\ \cline{1-1}  & 2000–2500 & 84,266 & 8.23\% & 29 & 8.41\% & 0.022 \\ \cline{1-1}  & 2500–3000 & 78,937 & 7.71\% & 29 & 8.41\% & 0.087 \\ \cline{1-1}  & \(>\)3000 & 647,122 & 63.17\% & 73 & 21.16\% & \(-\)1.094 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Cont. The results show that the \(q\) values for the distance from roads (\(q\) = 0.701), distance from main rivers (\(q\) = 0.626), and altitude (\(q\) = 0.555) are among the top three, indicating that these factors have the greatest impacts on landslides.