## 5 Discussion
### Best Model  
Figure 10 presents a comparison of the ROC curves for landslide susceptibility evaluation using three models in the Songpan and Mao County regions. In the Songpan region, all three models demonstrate excellent predictive performance, with the RF model (AUC = 0.917) performing the best, followed by the SVM model (AUC = 0.907) and the XGBoost model (AUC = 0.903). In the Mao County region, the RF model continues to maintain  
Figure 9: Mao County debris flow susceptibility maps.  
high predictive accuracy (AUC = 0.804), while the accuracy of the SVM (AUC = 0.710) and XGBoost (AUC = 0.766) models has slightly decreased compared to the Songpan region, though they remain at a good level overall.  
The Frequency Ratio (FR) values, calculated as the ratio of the percentage of landslide disaster points to the percentage of basin units within each susceptibility class, are shown in Figure 11. In the Songpan region, the SVM model still classifies some landslide points into the very-low-susceptibility class, and their FR values do not increase with the elevation of susceptibility levels. In contrast, the RF and XGBoost models mainly categorize landslide points into the very-high-susceptibility class, with the RF model achieving an FR value of 9.462, which is higher than that of XGBoost (FR = 5.647). This result further confirms the superiority of the RF model in the Songpan region.  
In the Mao County region, the FR values for the SVM model increase with the elevation of susceptibility levels, but the number of landslide points classified into the very-high-susceptibility class is very small. XGBoost categorizes the highest number of landslide points into the very-high-susceptibility class, but there are still some landslide points classified into the very-low-susceptibility class, and its FR values do not increase with the elevation of susceptibility levels. In contrast, the RF model does not classify any landslide points into the very-low-susceptibility class, and the number of landslide points in the very-high-susceptibility class is relatively high, with FR values increasing as susceptibility levels rise. This result also validates the superiority of the RF model in the Mao County region.  
The superior performance of the Random Forest (RF) model in this study can be attributed to the congruence between its algorithmic characteristics and the dataset's features. First, given that debris flow susceptibility is governed by complex nonlinear interactions among multiple factors, RF demonstrates efficacy in capturing these intricate patterns. Second, considering the potential redundancy or measurement noise inherent in the 17 input factors, RF's random feature subset selection (feature bagging) and majority voting decision mechanism endow it with inherent robustness against irrelevant features and noise. Third, RF mitigates model variance through the construction and integration of multiple weak learners, rendering it less susceptible to overfitting and enhancing generalization capability, particularly with a relatively limited sample size of disaster points (n = 44). This advantage stands in contrast to the Support Vector Machine (SVM), which encounters challenges in identifying optimal hyperplanes within high-dimensional spaces with small samples. As a  
Figure 10: Debris flow susceptibility ROC plots of (**a**) Songpan and (**b**) Mao County.  
boosting algorithm, XGBoost tends to construct stronger learners but may exhibit elevated variance, thereby harboring a potential risk of overfitting in small-sample scenarios. All models were subjected to grid search for parameter optimization, ensuring that each algorithm was evaluated under its optimal configuration for this specific dataset. The primacy of RF reflects its effective bias-variance trade-off under the data constraints of this study, rather than implying absolute superiority across all contexts.