## 5 Discussion
### Limitations and Future Work  
This study still has several limitations. Firstly, the automated watershed delineation method based on the digital elevation model (DEM) is constrained by fixed threshold settings, which often result in the fragmentation of complete debris flow gully systems into multiple sub-watershed units. Consequently, the evaluation results may not spatially align well with natural geomorphological entities. Although the manual integration of watersheds using high-resolution remote sensing imagery can effectively improve the integrity of evaluation units, this approach is time-consuming and conflicts with the efficiency advantages of automated modeling. Future research should focus on optimizing the delineation approach and threshold selection [87] to maintain the efficiency of automated extraction while enhancing the spatial consistency between evaluation units and natural geomorphology. This would better represent the integrity of gully systems and balance computational efficiency with spatial analysis accuracy, ultimately providing a more robust evaluation framework for geological hazard assessment in complex terrains.  
Secondly, the DEM data used in this study were acquired relatively early and may not accurately capture the dynamic changes in terrain and geomorphology within the study area. Long-term geological processes, climate variations, and human activities can lead to terrain alterations such as mountain erosion and land-use changes, causing discrepancies between DEM-derived terrain features (e.g., slope, aspect, terrain roughness) and current conditions. These discrepancies could potentially impact the accuracy of susceptibility assessments based on these data. Moreover, future work may consider integrating time-series InSAR deformation monitoring and multi-source remote sensing to develop dynamic terrain update models [88], thereby improving data responsiveness to anthropogenic and natural changes.  
Thirdly, the random selection method for negative samples applied in this study, while simple, is highly random and vulnerable to human interference, and so it may inadequately cover the characteristics of non-disaster regions. Another commonly used approach is generating negative samples outside buffer zones of positive samples; however, the absence of standardized buffer distances and the difficulty in adapting distance-based delineation to regional specifics may compromise reliability [89]. Furthermore, future research could attempt to use an iterative approach--first conducting susceptibility assessments using random sampling, then selecting negative samples from low-susceptibility areas in the results for a refined evaluation. This iterative process may optimize negative sample selection and enhance the accuracy and reliability of outcomes.  
Finally, compared to its performance in the Songpan region, the landslide susceptibility model showed a decline in accuracy during validation in Mao County. To further optimize the model and improve its predictive capability and reliability in different regions, future research could expand the validation case library to include multiple landform types in western Sichuan, providing a comprehensive assessment of the model's performance in complex and diverse terrain conditions. Additionally, exploring new indicators, such as tourist heat intensity, in the evaluation system could reveal their potential correlation with landslide susceptibility, enhancing the model's ability to assess debris flow risk in different regions, and providing a more reliable basis for landslide disaster prevention and mitigation.