## 4 Results and Discussion
### The Predictive Performance  
Figure 10 presents the ROC curves of all models under both GLHM and Random sampling strategies, along with their corresponding Area Under the Curve (AUC) values. All models showed acceptable to excellent classification ability, with AUC values above 0.80, indicating their effectiveness in distinguishing landslide-prone areas. Among the GLHM-based models, XGBoost-GLHM achieved the highest AUC value (0.9861), followed by SVM-RF-GLHM (0.9683), SVM-GLHM (0.9107), and NB-GLHM (0.8963). The ensemble learning models (XGBoost, SVM-RF) clearly outperformed the traditional models (e.g., Naive Bayes and SVM), highlighting their superior capability in capturing complex feature interactions. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Zoning**} & \multirow{2}{*}{**Area (km\({}^{2}\))**} & \multirow{2}{*}{**Area Proportion**} & \multirow{2}{*}{\begin{tabular}{c} **Number of** \\ **Landslide Points** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Landslide** \\ **Point Proportion** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Landslide** \\ **Point Density** \\ **(Point/km\({}^{2}\))** \\ \end{tabular} } \\ \hline \multirow{4}{*}{XGBoost-Random} & Very low & 407.70 & 18.41\% & 5 & 4.90\% & 0.0123 \\  & Low & 534.19 & 24.12\% & 6 & 5.88\% & 0.0112 \\ \cline{1-1}  & Medium & 873.28 & 39.43\% & 33 & 32.35\% & 0.0378 \\ \cline{1-1}  & High & 251.73 & 11.36\% & 32 & 31.37\% & 0.1271 \\ \cline{1-1}  & Severe & 148.11 & 6.69\% & 26 & 25.49\% & 0.1755 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._  
Figure 10: ROC curves with AUC values. Under the Random sampling strategy, XGBoost-Random still achieved the highest AUC (0.9746), followed by SVM-RF-Random (0.9362), SVM-Random (0.8440), and NB-Random (0.8190). While the performance of simpler models such as NB and SVM declined under Random sampling, ensemble models like XGBoost and SVM-RF remained robust and continued to deliver high classification accuracy. Among all the evaluated models, ensemble learning approaches--particularly XGBoost and SVM-RF--consistently achieved the highest AUC values across both sampling strategies, highlighting their strong overall performance. These models' ability to capture nonlinear relationships and effectively integrate multiple predictive features likely contributed to their superior classification accuracy. Notably, XGBoost maintained top performance under both GLHM (AUC = 0.9861) and Random (AUC = 0.9746) sampling, while SVM-RF also delivered high AUC scores (0.9683 and 0.9362, respectively), underscoring their robustness even in the absence of spatially structured data. In contrast, traditional models such as Naive Bayes and SVM exhibited more pronounced performance declines under Random sampling, suggesting greater sensitivity to sampling variability. Overall, the consistent and high performance of XGBoost and SVM-RF indicates their strong generalization capacity and practical utility for landslide susceptibility mapping in diverse scenarios. To further explore differences between sampling methods and models, we conducted statistical significance tests. The data used for these tests were obtained from 20 repetitions of 5-fold spatial cross-validation. Figures 11 and 12 show boxplots of AUROC and accuracy for models under different sampling methods. Table 3 presents Mann-Whitney U test results comparing performance across sampling strategies, while Table 4 shows Kruskal-Wallis test results for model comparisons. These tests evaluate whether the differences observed in model performance are statistically significant. Figure 11: Box-and-whisker plot of area under the receiver operating characteristic curve 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.