A global approach for mapping multi-hazard susceptibility using deep learning: A case study in Japan

This report provides a globally applicable approach for multi-hazard susceptibility mapping based on deep learning and demonstrate its use on a range of geophysical, atmospheric, and hydrological hazards using Japan as case study. Disaster preparedness is a key determinant in reducing multi-hazard risk by enabling timely and effective responses. A crucial, yet often missing, component of disaster preparedness is the development of a multi-hazard susceptibility map, which can be interpreted as the spatially distributed probability of occurrences of hazards.

The analysis reveals that susceptibility to individual hazards varies widely across Japan, influenced by factors such as terrain, subsurface conditions, atmospheric patterns, and lithology. The multi-hazard susceptibility map highlights diverse susceptibility levels, with southern Japan showing particularly high susceptibility that is mainly driven by the combined effects of heatwaves and earthquakes. The insights from our multi-hazard susceptibility map can help prioritize resources to the most vulnerable areas and support targeted resilience-building efforts in communities facing multiple hazards.