## 5 Discussion
### Limitations and Future Directions  
Although the proposed fuzzy membership-based credibility quantification method for negative samples exhibits significant advantages in terms of enhancing representational capacity (spatial continuity and reflection of potential landslide distribution) and achieving quantitative reliability assessment of negative samples, several noteworthy limitations remain to be addressed in future research:  
\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 & 0.855 & 0.854 & 0.938 & 0.946 & 0.966 \\ Recall & 0.891 & 0.912 & 0.942 & 0.896 & 0.792 \\ F1 score & 0.873 & 0.882 & 0.940 & 0.920 & 0.870 \\ AUC & 0.872 & 0.885 & 0.939 & 0.902 & 0.874 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Validation of FR-SVM performance under negative sample credibility thresholds.  
The construction of negative sample credibility maps inherently heavily relies on the selected evaluation factors (in this study: elevation, SEI, and SMC). Although the factor selection process was optimized by rigorous discriminative power analysis, nonlinear correlation assessment, and collinearity diagnostics, this dependency is unavoidable because credibility is computed within the environmental space defined by these factors [14; 31]. Consequently, significant changes in the geological-geographical settings, dominant landslide mechanisms, or key triggering factors of a study area may necessitate re-evaluation and screening of the most relevant evaluation factors to ensure the validity of the constructed credibility map. Future research should validate the proposed approach across more diverse regions and explore the establishment of adaptive factor screening frameworks that respond to region-specific controlling factors.  
The core of the proposed method lies in constructing fuzzy membership functions by fitting the functional relationships between the evaluation factors and the landslide frequency, thereby integrating the statistical patterns into fuzzy theory [32]. However, the selection of optimal function forms (e.g., linear, sigmoid, and Gaussian) and the parameter determination process introduce subjectivity [24]. Different functional forms may yield subtle variations in the credibility computation results. Although this study aimed to capture nonlinear relationships between factors and negative samples, establishing more objective and robust fuzzy membership functions to reduce uncertainty due to subjectivity remains a critical issue that warrants in-depth exploration in future research.  
Although the proposed negative sample sampling method demonstrates universal applicability across two mainstream ML models, namely, SVM and RF, with consistent optimal threshold intervals, significant differences exist in their responsiveness to negative sample quality variations (e.g., RF exhibits superior stability and predictive capability). This indicates that the precision and robustness of the final landslide susceptibility mapping results are significantly influenced by the inherent characteristics of the selected ML model. These findings imply that extensively adopted general-purpose ML models (e.g., SVM and RF) exhibit substantial disparities in capturing the intrinsic complex spatial dependencies and multisource data integration requirements of landslide susceptibility assessment [33]. Consequently, there is an urgent need for an in-depth exploration of the application efficacy of diverse ML models in this domain [34].