## 4 Results and Discussion
### The Predictive Performance
Figure 12: Box-and-whisker plot of accuracy for landslide susceptibility models using different sampling methods for non-landslide points. (**a**) GLHM-based non-landslide sampling method; (**b**) Random non-landslide sampling method. The comparison between sampling methods reveals significant differences in model performance across all tested algorithms. Models trained with GLHM-informed sampling consistently outperformed those using random sampling within the AOI in both AUROC and accuracy metrics. For AUROC, the NB model showed a notable improvement, increasing from a median of 82.05% (IQR 4.29) with random sampling to 90.49% (IQR 3.44) under GLHM sampling, representing a significant gain of +8.44% (\(p<0.001\)). Similarly, the SVM model improved by +7.11% (\(p<0.001\)), moving from 83.71% (IQR 5.75) to 90.82% (IQR 2.93). The SVM-RF model also saw an improvement of +3.45% (\(p=0.023\)), and XGBoost improved by +3.04% (\(p=0.029\)). Accuracy gains followed the same trend. The NB model increased by +11.30%, from 71.31% (IQR 2.68) to 82.62% (IQR 0.76), which was highly significant (\(p<0.001\)). The SVM model showed an +8.33% increase (from 74.51% to 82.84%, \(p<0.001\)), while SVM-RF improved by +7.40% (\(p=0.002\)), and XGBoost increased by +8.31% (\(p<0.001\)). These results indicate that the GLHM-based sampling strategy leads to statistically significant enhancements in model predictive performance compared to random sampling. This suggests that sampling guided by hazard-level information improves the quality of the training data, enabling models to better distinguish between landslide and non-landslide areas. Table 4 presents the results of the pairwise statistical comparisons among the evaluated models for both AUROC and Accuracy metrics, under different sampling strategies. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multirow{2}{*}{**Model Pair**} & \multicolumn{3}{c}{**AUROC (\%)**} & \multicolumn{3}{c}{**Accuracy (\%)**} \\ \cline{2-6}  & **Significance** & **Adj.** & **Significance (Bonferom)** & **Significance** & **Adj.** & **Significance (Bonferom)** \\ \hline _GLHM_ & & & & & & \\ NB-SVM & 1.000 & 1.000 & SVM-RF-NB & 0.007 & 0.044 \\ NB-SVM-RF & 0.074 & 0.442 & SVM-RF-SVM & 0.074 & 0.442 \\ NB-XGBoost & 0.007 & 0.044 \({}^{*}\) & SVM-RF-XGBoost & 0.007 & 0.044 \({}^{*}\) \\ SVM-SVM-RF & 0.371 & 1.000 & NB-SVM & 0.371 & 1.000 \\ SVM-XGBoost & 0.074 & 0.442 & NB-XGBoost & 0.007 & 0.044 \({}^{*}\) \\ _Random_ & & & & & \\ NB-SVM & 0.074 & 0.442 & NB-SVM-RF & 0.371 & 1.000 \\ NB-SVM-RF & 0.007 & 0.044 \({}^{*}\) & NB-SVM & \textless{}0.001 & \textless{}0.001 \({}^{***}\) \\ NB-XGBoost & \textless{}0.001 & \textless{}0.001 \({}^{***}\) & NB-XGBoOST & 0.007 & 0.044 \({}^{*}\) \\ SVM-SVM-RF & 0.007 & 0.044 \({}^{*}\) & SVM-RF-SVM & 1.000 & 1.000 \\ SVM-XGBoost & \textless{}0.001 & \textless{}0.001 \({}^{***}\) & SVM-RF-XGBoOST & 0.371 & 1.000 \\ SVM-RF-XGBoost & 0.371 & 1.000 & SVM-XGBoOST & 0.371 & 1.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Statistical significance of pairwise model comparisons. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**GLHM**} & \multicolumn{2}{c}{**Random**} & \multirow{2}{*}{\(\mathbf{\Delta}\)} & \multirow{2}{*}{\(\mathbf{p}\)**-Values**} \\  & **Median (IQR)** & **Median (IQR)** & & & \\ \hline _AUROC_(\%) & & & & \\ NB & 90.49 (3.44) & 82.05 (4.29) & +8.44 & \textless{}0.001 *** \\ SVM & 90.82 (2.93) & 83.71 (5.75) & +7.11 & \textless{}0.001 *** \\ SVM-RF & 91.67 (2.83) & 88.22 (3.43) & +3.45 & 0.023 \({}^{*}\) \\ XGBoost & 94.61 (3.58) & 91.56 (4.51) & +3.04 & 0.029 \({}^{*}\) \\ _Accuracy_(\%) & & & & \\ NB & 82.62 (0.76) & 71.31 (2.68) & +11.30 & \textless{}0.001 *** \\ SVM & 82.84 (1.84) & 74.51 (2.48) & +8.33 & \textless{}0.001 *** \\ SVM-RF & 81.59 (2.13) & 74.20 (4.70) & +7.40 & 0.002 \({}^{**}\) \\ XGBoost & 84.30 (2.81) & 75.99 (2.85) & +8.31 & \textless{}0.001 *** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Model performance comparison across sampling strategies.