Using rapid damage observations for Bayesian updating of hurricane vulnerability functions: A case study of Hurricane Dorian using social media

Rapid impact assessments immediately after disasters are crucial to enable rapid and effective mobilization of resources for response recovery efforts. These often performed by analysing the three components risk: hazard, exposure vulnerability. Vulnerability curves constructed using historic insurance data or expert judgments, reducing their applicability characteristics specific hazard building stock. Therefore, this paper outlines an approach creation event-specific vulnerability curves, Bayesian statistics (i.e., zero-one inflated beta distribution) update a pre-existing curve prior) with observed derived from social media. The is applied in case study Hurricane Dorian, which hit Bahamas September 2019. We analysed footage shot predominantly unmanned aerial vehicles (UAVs) other airborne posted on YouTube first 10 days disaster. Due its nature, can be used regardless amount available as it balances contribution prior observations.