Preprints
https://doi.org/10.5194/nhess-2020-282
https://doi.org/10.5194/nhess-2020-282
25 Sep 2020
 | 25 Sep 2020
Status: this preprint was under review for the journal NHESS but the revision was not accepted.

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

Jens A. de Bruijn, James E. Daniell, Antonios Pomonis, Rashmin Gunasekera, Joshua Macabuag, Marleen C. de Ruiter, Siem Jan Koopman, Nadia Bloemendaal, Hans de Moel, and Jeroen C. J. H. Aerts

Abstract. 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.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Jens A. de Bruijn, James E. Daniell, Antonios Pomonis, Rashmin Gunasekera, Joshua Macabuag, Marleen C. de Ruiter, Siem Jan Koopman, Nadia Bloemendaal, Hans de Moel, and Jeroen C. J. H. Aerts
 
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
Jens A. de Bruijn, James E. Daniell, Antonios Pomonis, Rashmin Gunasekera, Joshua Macabuag, Marleen C. de Ruiter, Siem Jan Koopman, Nadia Bloemendaal, Hans de Moel, and Jeroen C. J. H. Aerts
Jens A. de Bruijn, James E. Daniell, Antonios Pomonis, Rashmin Gunasekera, Joshua Macabuag, Marleen C. de Ruiter, Siem Jan Koopman, Nadia Bloemendaal, Hans de Moel, and Jeroen C. J. H. Aerts

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Latest update: 26 May 2024
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Short summary
Following hurricanes and other natural hazards, it is important to quickly estimate the damage caused by the hazard such that recovery aid can be granted from organizations such as the European Union and the World Bank. To do so, it is important to estimate the vulnerability of buildings to the hazards. In this research, we use post-disaster observations from social media to improve these vulnerability assessments and show its application in the Bahamas following Hurricane Dorian.
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