Explainable machine learning for more robust models of tsunami-induced fatalities

Abstract Understanding the factors that control tsunami-induced fatalities remains a major challenge due to the complex, nonlinear interplay between hazard intensity, evacuation dynamics and social vulnerability. Previous empirical approaches, largely based on simple regressions, have offered valuable insights but remain limited in their ability to represent threshold effects and context-specific interactions. In this study, an explainable machine learning framework is applied to the 2011 Tōhoku