Probabilistic tsunami fragility: an uncertainty-aware framework for disaggregated building damage estimation

Tsunamis, while infrequent, can be some of the most damaging natural hazards to coastal communities. The unpredictable nature of tsunamis necessitates the development of robust methods that quantify probable damage and inform the development of mitigation strategies. While this critical need is recognized by the scientific community, probabilistic frameworks to assess risk given the range of tsunami hazards are still in their infancy. Tsunami Fragility Functions represent the state-of-the-art for building fragility estimation, they link hazard to risk, and are central to these frameworks. These models provide a notion of the expected building damage given a measure of the tsunami. However, they have seldom been studied directly as building damage estimators, hence their capacity to inform downstream tasks remains largely untested. Furthermore, the current state-of-the-art lacks a method to handle input and output uncertainty, which is crucial for holistic frameworks such as recently proposed probabilistic Hazard-to-Risk frameworks. In the present work we propose a novel bayesian ordinal regression framework, inspired by fragility functions, for disaggregated building damage estimation to fulfill this need. We fit our method on 2011 Tohoku Earthquake and Tsunami data and provide three case studies to highlight the model’s generalization capabilities and limitations.