## 4 Results
### Uncertainty in Machine Learning Model Selection  
To reduce the impact of model selection uncertainty on landslide susceptibility predictions, the average landslide susceptibility index for each raster cell was computed using susceptibility maps generated with the \"Slope\" dataset and the SVM, GBDT, and RF models, collectively referred to as the \"Average\" model. The resulting landslide susceptibility map for Badong County is shown in Figure 8a, and the corresponding ROC curve is presented in Figure 8b. Compared with the individual models, the average method achieved an AUC value of 0.910, reflecting a significant improvement. This demonstrated the effectiveness  
Figure 6: Susceptibility zoning maps generated by different machine learning models based on two sampling methods: (**a**) Buffer-LR; (**b**) Slope-LR; (**c**) Buffer-SVM; (**d**) Slope-SVM; (**e**) Buffer-GBDT; (**f**) Slope-GBDT; (**g**) Buffer-RF; and (**h**) Slope-RF. Figure 7: ROC curves and AUC values for each model based on two datasets: (**a**) Buffer dataset; (**b**) Slope dataset. of the average method in providing a comprehensive analysis of landslide susceptibility results across different models. The prediction results were evaluated by analyzing the distribution and proportion of existing landslide disaster points across different susceptibility zones. To this end, the proportion of landslides within each susceptibility zone was calculated. The study area was divided into five susceptibility zones (VL, L, M, H, and VH) by using the natural break method. Statistical analysis was performed on the distribution of landslide disaster points, distribution of raster cells within each susceptibility zone generated by each model, and corresponding FR indicators (Table 5). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{\begin{tabular}{c} **Susceptibility** \\ **Zones** \\ \end{tabular} } & \multirow{2}{*}{**Raster Cells**} & \multirow{2}{*}{\begin{tabular}{c} **Proportion** \\ **(\%)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Landslide** \\ **Count** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Proportion** \\ **(\%)** \\ \end{tabular} } & \multirow{2}{*}{**FR**} \\ \hline \multirow{4}{*}{Slope-SVM} & VL & 1,371,117 & 36.84 & 26 & 4.74 & 0.13 \\  & L & 827,852 & 22.25 & 55 & 10.04 & 0.45 \\  & M & 559,434 & 15.03 & 68 & 12.41 & 0.83 \\  & H & 462,794 & 12.44 & 106 & 19.34 & 1.56 \\  & VH & 500,315 & 13.44 & 293 & 53.47 & 3.98 \\ \hline \multirow{4}{*}{Slope-RF} & VL & 1,168,148 & 31.39 & 14 & 2.55 & 0.11 \\  & L & 919,426 & 24.71 & 28 & 5.11 & 0.32 \\  & M & 710,896 & 19.10 & 78 & 14.23 & 0.75 \\  & H & 526,665 & 14.15 & 126 & 22.99 & 1.56 \\  & VH & 396,377 & 10.65 & 302 & 55.11 & 4.56 \\ \hline \multirow{4}{*}{Slope-GBDT} & VL & 1,381,568 & 37.12 & 22 & 4.01 & 0.08 \\  & L & 757,638 & 20.36 & 36 & 6.57 & 0.21 \\ \cline{1-1}  & M & 644,561 & 17.32 & 71 & 12.96 & 0.75 \\ \cline{1-1}  & H & 477,588 & 12.83 & 110 & 20.07 & 1.62 \\ \cline{1-1}  & VH & 460,157 & 12.36 & 309 & 56.39 & 5.17 \\ \hline \multirow{4}{*}{Average} & VL & 1,665,281 & 44.75 & 23 & 4.20 & 0.09 \\  & L & 688,898 & 18.51 & 35 & 6.39 & 0.35 \\ \cline{1-1}  & M & 524,041 & 14.08 & 60 & 10.95 & 0.78 \\ \cline{1-1}  & H & 420,368 & 11.30 & 109 & 19.89 & 1.67 \\ \cline{1-1}  & VH & 422,924 & 11.36 & 321 & 58.58 & 5.44 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Statistical analysis of landslide disaster points, raster cells, and FR indicators in different susceptibility zones across various models. Figure 8: (a) LSM obtained using the average method; (b) the corresponding ROC curve and AUC values. Across all models, the distribution of landslide disaster points increased with higher susceptibility levels within the study area. In all four scenarios, the proportion of landslide disasters in very-low-susceptibility areas remained below 5% (Figure 9), with the RF model showing the lowest proportion at 2.55%.