Snow Avalanche Susceptibility Evaluation and Determination of Critical Influencing Factors in the Eastern Himalayan Syntaxis Region

Under global warming conditions, assessing snow avalanche susceptibility and identifying critical influencing factors in the snow-covered regions of the Tibetan Plateau is fundamental to preventing snow avalanche disaster risks. This study focuses on the Eastern Himalayan Syntaxis (EHS) region and collected 449 snow avalanche events through event collection, field investigations, and remote sensing interpretation. Using these data, the study employed four machine learning models—Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), and Gradient Boosting Decision Tree (GBDT)—to produce snow avalanche susceptibility maps. Furthermore, the SHAP algorithm was utilized to identify the critical factors affecting snow avalanche susceptibility in the EHS. Under the random split, the RF model achieved the highest AUC value (0.912). Under spatial block cross-validation, the AUC values decreased to varying degrees, with SVM and RF still performing best (AUC = 0.855 and 0.850, respectively). The total area of extremely high and high-risk snow avalanche zones was 4036.4 km2, accounting for 21.0% of the study area. High-risk snow avalanche areas were primarily concentrated in the narrow valley regions near the entrances of the Doxiongla and Galongla tunnels. Additionally, the use of the SHAP algorithm enhanced the interpretability of the RF model. The study found that NDVI, glacial kernel density, January average temperature, land use, slope, and aspect were the main factors predicting snow avalanche occurrence, reaching a cumulative contribution rate of 66.4%. These findings not only confirm the effectiveness of the combination of machine learning models and the SHAP algorithm in snow avalanche susceptibility assessment but also delineate critical areas for enhancing local avalanche disaster prevention and mitigation strategies. The results of this study can serve as a reference for other mountainous regions with insufficient snow avalanche research data.