## 4 Results
### Optimal Credibility Threshold
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Factor** & **Zone I** & **Zone II** & **Zone III** & **Zone IV** & **Zone V** \\ \hline \multirow{3}{*}{Model A} & A & 0.02 & 0.10 & 0.08 & 0.08 & 0.72 \\  & B & 0.40 & 0.20 & 0.07 & 0.04 & 0.29 \\  & A/B & 0.05 & 0.51 & 1.10 & 1.89 & 2.47 \\ \hline \multirow{3}{*}{Model B} & A & 0.03 & 0.03 & 0.10 & 0.09 & 0.76 \\  & B & 0.44 & 0.09 & 0.09 & 0.06 & 0.32 \\  & A/B & 0.06 & 0.34 & 1.07 & 1.46 & 2.38 \\ \hline \multirow{3}{*}{Model C} & A & 0.02 & 0.03 & 0.04 & 0.10 & 0.82 \\  & B & 0.38 & 0.08 & 0.08 & 0.10 & 0.37 \\  & A/B & 0.05 & 0.36 & 0.51 & 1.00 & 2.24 \\ \hline \multirow{3}{*}{Model D} & A & 0.04 & 0.04 & 0.03 & 0.04 & 0.85 \\  & B & 0.37 & 0.09 & 0.04 & 0.04 & 0.45 \\  & A/B & 0.10 & 0.43 & 0.84 & 0.90 & 1.88 \\ \hline \multirow{3}{*}{Model E} & A & 0.04 & 0.03 & 0.03 & 0.02 & 0.88 \\  & B & 0.38 & 0.07 & 0.03 & 0.02 & 0.50 \\ \cline{1-1}  & A/B & 0.10 & 0.45 & 1.03 & 0.99 & 1.77 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Susceptibility zone statistics and landslide ratio of FR–RF models. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Verification Method** & **Model A** & **Model B** & **Model C** & **Model D** & **Model E** \\ \hline Precision & 0.928 & 0.926 & 0.938 & 0.975 & 0.974 \\ Recall & 0.839 & 0.936 & 0.942 & 0.883 & 0.889 \\ F1-score & 0.881 & 0.926 & 0.940 & 0.927 & 0.928 \\ AUC & 0.887 & 0.925 & 0.941 & 0.932 & 0.937 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Impact analysis of the negative sample credibility threshold on FR–RF model performance. with credibility thresholds of 0.8-1 (Model D) or 0.9-1 (Model E), the negative and positive samples exhibited significantly different characteristics. This allows the model to distinguish between them accurately and achieve very high accuracy. However, the negative samples only represent the typical characteristics of nonlandslide areas in the study area. The model may increase the high-susceptibility zone by lowering the classification threshold, which may result in FP and an overestimation of susceptibility in the study area. In conclusion, the optimal threshold for selecting negative samples in the study area is 0.7-1 (Model C). Based on the dual principles of minimizing the area proportions of the high-/very-high-susceptibility zones and maximizing the concentration of landslide distribution (landslide ratio), this study comprehensively ranked the candidate models. The area proportions of the high-/very-high-susceptibility zones across the models were ranked in descending order as follows: Model E (0.52) > Model D (0.49) > Model C (0.47) > Model B (0.38) > Model A (0.36). The landslide ratios in areas of high and extremely high susceptibility for each model, ranked from highest to lowest (Table 4), are as follows: Model C (0.92) > Model E (0.90) > Model D (0.89) > Model B (0.85) > Model A (0.80). Based on further analysis, only Models B, C, and D strictly comply with the distribution requirement that the landslide ratio value increases monotonically with susceptibility levels and exhibits high concentrations in higher-susceptibility zones (high and very high). Through a comprehensive analysis of these indicators and their conformity to the distribution patterns, Model C, constructed by selecting negative samples within the credibility threshold range of 0.7-1, was identified as the optimal landslide susceptibility evaluation model. Model C not only demonstrates exceptional performance (high-credibility differentiation ability) and reliability within this threshold but also produces the most rational susceptibility zoning results (Figure 9), namely, high- and very-high-susceptibility zones occupy a moderate proportion (47%), while concentrating the vast majority of historical landslide events (92%) in the study area. Based on the optimal model (Model C), this study generated a high-precision spatial distribution map of landslide susceptibility (Figure 9).