Preprints
https://doi.org/10.5194/nhess-2020-282
https://doi.org/10.5194/nhess-2020-282

  25 Sep 2020

25 Sep 2020

Review status: this preprint is currently under review for the journal NHESS.

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

Jens A. de Bruijn1,2, James E. Daniell1,3, Antonios Pomonis1, Rashmin Gunasekera1, Joshua Macabuag1, Marleen C. de Ruiter2, Siem Jan Koopman4,5,6, Nadia Bloemendaal2, Hans de Moel2, and Jeroen C. J. H. Aerts2,7 Jens A. de Bruijn et al.
  • 1World Bank Group 1818 H St NW, Washington DC, 20433, USA
  • 2Institute for Environmental Studies IVM, VU University, Amsterdam, The Netherlands
  • 3Geophysical Institute and Center for Disaster Management and Risk Reduction Technology, Karlsruhe Institute of Technology, Karlsruhe, Germany
  • 4Department of Econometrics and Data Science, School of Business and Economics, Vrije Universiteit Amsterdam, The Netherlands
  • 5CREATES, Aarhus University, Denmark
  • 6Tinbergen Institute Amsterdam, The Netherlands
  • 7Deltares, Delft, The Netherlands

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.

Jens A. de Bruijn et al.

 
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Jens A. de Bruijn et al.

Jens A. de Bruijn et al.

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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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