Development of a Probabilistic Snow Disaster Risk Assessment Framework Integrating Hazard and Exposure: The Maximum Disaster Spatial Density (MDSD) Optimization Approach

Snow disasters, intensified by climate change, pose increasing threats to infrastructure and socio-economic systems. However, conventional risk assessments often rely heavily on meteorological hazards or static topographic factors, frequently overlooking the critical role of exposure in urban environments. This study introduces the Maximum Disaster Spatial Density (MDSD) method, a novel optimization framework designed to identify snow disaster-prone areas by integrating historical disaster records (2009–2018) with climatic, topographic, and social variables. Using datasets covering South Korea, including radar-based precipitation, temperature, MODIS-based Normalized Difference Snow Index (NDSI), elevation, and building density, the MDSD algorithm iteratively optimizes feature weights to maximize disaster density within high-risk clusters. Our analysis reveals that precipitation and building density are the dominant determinants of snow disaster vulnerability, whereas elevation and satellite-based snow cover duration show relatively lower importance. This finding challenges the traditional assumption that high-altitude mountainous regions are inherently more vulnerable, quantitatively demonstrating that disaster risk is driven by the intersection of extreme weather and built-environment exposure. To address model uncertainty, we applied an ensemble approach with 20 realizations, generating a probabilistic snow disaster risk map (0–1 scale). This map effectively highlights high-risk zones, including coastal urban areas, which were previously underestimated by topography-based assessments. Furthermore, we propose a dual-track disaster response strategy by integrating this static risk map as a priority filter into a real-time monitoring system. This framework enables decision-makers to prioritize resource allocation to high-exposure areas during extreme snow events, bridging the gap between scientific risk assessment and practical disaster management.

 

Acknowledgements

This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT)(RS-2025-00518650).