Machine learning-based identification and assessment of snow disaster risks using multi-source data: Insights from Fukui prefecture, Japan

Understanding the driving factors behind snowstorm risk and their nonlinear effects is critical for developing effective response strategies. This study, focusing on the 2018 Fukui snowstorm in Japan, integrates multi-source data, including mobile GPS data, Digital Elevation Model (DEM) data, road data, urban data, and traffic congestion data, to develop an interpretable model for quantifying high-risk areas and examining key nonlinear relationships and threshold effects influencing snowstorm impact occurrence, offering actionable insights for mitigation strategies. We employed four machine learning models—Decision Tree, Random Forest, Multilayer Perceptron (MLP), and Extreme Gradient Boosting (XGBoost)—to capture complex nonlinear relationships among influencing factors and applied SHAP (SHapley Additive exPlanations) theory to interpret variable contributions. The results reveal that: (1) compared to Random Forest, Decision Tree, and MLP models, the XGBoost model demonstrates superior performance with a prediction accuracy of 0.8225; (2) factors such as elevation, slope, road density, and road width exhibit significant nonlinear impacts and threshold effects on snowstorm impact occurrence; (3) Urban areas with elevation below $51.9m$, slopes exceeding ${9.9}^{°}$, a density of major roads (Road Type 1) less than $443.75m/{\mathrm{km}}^2$, a density of minor roads (Road Type 2) less than $550.25m/{\mathrm{km}}^2$, and where rural roads (Road Type 3) are nearly absent, along with population fluctuations ranging between $\left(-\mathrm{0.25,0}\right)$, are particularly vulnerable to snow disasters. In contrast, areas with flat terrain and high densities of rural roads are less likely to be affected; and (4) snow disaster resilience in mitigating traffic congestion can be improved by monitoring GPS data for early warnings and optimizing the sp. configuration of major and minor roads.