Machine learning and hydrodynamic proxies for enhanced rapid tsunami vulnerability assessment

Coastal communities in various regions of the world are exposed to risk from tsunami inundation, requiring reliable modeling tools for implementing effective disaster preparedness and management strategies. This study advocates for comprehensive multi-variable models and emphasizes the limitations of traditional univariate fragility functions by leveraging a large, detailed dataset of ex-post damage surveys for the 2011 Great East Japan tsunami, hydrodynamic modeling of the event, and advanced machine learning techniques. It investigates the complex interplay of factors influencing building vulnerability to tsunami, with a specific focus on the hydrodynamic effects associated to tsunami propagation on land. Novel synthetic variables representing shielding and debris impact mechanisms prove to be suitable proxies for water velocity, offering a practical solution for rapid damage assessments, especially in post-event scenarios or large-scale analyses. Machine learning then emerges as a promising approach to tackle the complexities of vulnerability assessment, while providing valuable and interpretable insights.Hydrodynamic modelling and machine learning-based methods can effectively model tsunami damage mechanisms and represent an improvement over traditional univariate fragility functions for vulnerability assessments.