Using rapid damage observations for Bayesian updating of hurricane vulnerability functions

A case study of Hurricane Dorian using social media

Bibliographic Data

ID22025870
AuthorsJens de Bruijn (0000-0003-3961-6382, World Bank Group, corresponding author), James Daniell (0000-0001-8299-0535, Karlsruhe Institute of Technology), James E Daniell, Antonios Pomonis (World Bank Group), Rashmin Gunasekera (0000-0003-1244-3039, World Bank Group), Joshua Macabuag, Josh Macabuag (0000-0002-2632-0294, World Bank Group), Marleen de Ruiter (0000-0001-5991-8842, Vrije Universiteit Amsterdam), Siem Jan Koopman (0000-0002-4440-9524, Tinbergen Institute), Nadia Bloemendaal (0000-0002-3418-8692, Vrije Universiteit Amsterdam), Hans de Moel (0000-0002-6826-1974, Vrije Universiteit Amsterdam), Jeroen C J H Aerts (0000-0002-2162-5814, Deltares)
Year2022
Volume72
Pages102839
Publication date2022-04-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueInternational Journal of Disaster Risk Reduction (JOURNAL)
Journal identifiersISSN: 2212-4209
PublisherElsevier BV (PUBLISHER)
DOI10.1016/j.ijdrr.2022.102839
OpenAlexW4210360663
LanguageEN
Citations received4
References cited47

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

Bayesian probability · Computer security · Hazard · Social vulnerability · Vulnerability assessment · Computer Science · Disaster Management and Resilience · Environmental Science · Flood Risk Assessment and Management · Psychology · Tropical and Extratropical Cyclones Research · Artificial Intelligence

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