## 6 Conclusions  
This study conducted a Pearson correlation analysis to select 17 key feature factors from four dimensions: topography, geology, environmental factors, and human activities. Three machine learning models--Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)--were employed to construct a landslide susceptibility model for the Songpan region. The resulting model was then applied to the Mao County region to validate its generalization ability in other study areas. Based on the landslide susceptibility evaluation results for both Songpan and Mao County, the best model was selected based on predictive accuracy and robustness. Finally, an importance ranking of the landslide susceptibility factors was performed. The results indicate the following:  
1. In the Songpan region, high landslide susceptibility areas are primarily located in the central, southern, and northeast-southeast transition zones of the study area, while low-susceptibility areas are concentrated in the northwest plateau region. In the Mao County region, high-susceptibility areas are concentrated in the central fault basin, southern deeply cut river valleys, and along the eastern fault zone in a strip-like distribution. Low-susceptibility areas are mainly found in the western plateau and northern folded mountains. This spatial distribution pattern highly matches the spatial distribution characteristics of landslide disaster points, thus validating the scientific and reliable evaluation method used in this study. 2. The study results highlight the significant influences of factors such as elevation, seismic nucleation density, population density, and distance to roads on landslide susceptibility, further revealing the main controlling factors of landslide disasters in the region. 3. Compared to the Support Vector Machine model and Extreme Gradient Boosting model, the Random Forest model demonstrated better applicability in both the Songpan and Mao County regions. It exhibited greater advantages in landslide susceptibility prediction tasks and is a more suitable choice for landslide susceptibility analysis in the complex geological environment of western Sichuan. This provides a cross-regional adaptive technical framework and quantitative evaluation paradigm for risk prevention and control in typical geological disaster-prone areas, such as active regions influenced by monsoon climates. The debris flow susceptibility maps generated by this study provide spatial decision-making support for local disaster management authorities (e.g., natural resource bureaus and emergency management agencies). High-susceptibility zones should be designated as avoidance areas for land-use planning and infrastructure development, requiring implementation of stringent engineering control measures when construction is unavoidable. These areas warrant prioritized installation of specialized monitoring equipment (e.g., rainfall gauges, debris flow detectors, video surveillance systems) and community-based monitoring networks. The zoning results enable the formulation of granular emergency response plans at township levels, with explicit delineation of evacuation routes and shelter locations tailored to different risk classifications. Furthermore, these zoning maps should be integrated into public awareness campaigns to enhance risk perception and self-rescue capabilities among residents in high-risk areas. The transfer learning framework employed in this research holds promise for application in analogous geological settings throughout southwestern Sichuan, providing robust technical support for regional geohazard risk assessment and disaster prevention initiatives. T.L. and Q.H. : Data curation, Methodology, Software, Writing--original draft. Q.C. : Conceptualization, Methodology, Supervision. All authors have read and agreed to the published version of the manuscript.