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

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; Jeroen C.J.H. Aerts

doi:10.1016/j.ijdrr.2022.102839

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

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, Jeroen C.J.H. Aerts

^*Corresponding author for this work

Research output: Contribution to Journal › Article › Academic › peer-review

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.

Original language	English
Article number	102839
Pages (from-to)	1-16
Number of pages	16
Journal	International Journal of Disaster Risk Reduction
Volume	72
Early online date	5 Feb 2022
DOIs	https://doi.org/10.1016/j.ijdrr.2022.102839
Publication status	Published - 1 Apr 2022

Bibliographical note

Funding Information:
Our research was funded by an NWO-Vici grant from the Netherlands Organisation for Scienti?c Research (NWO; grant number 453-14-006) and an EU-ENHANCE grant from the European Community's Seventh Framework Programme (FP7; grant number 308438). This work was conducted by the World Bank's Disaster Resilience Analytics and Solutions (D-RAS) Knowledge Silo Breaker (KSB), under the Global Practice for Urban, Disaster Risk Management, Resilience and Land (GPURL). This research has been funded by the World Bank and the Global Facility for Disaster Reduction and Recovery (GFDRR) grant (TF0B1140).

Funding Information:
Our research was funded by an NWO-Vici grant from the Netherlands Organisation for Scientiﬁc Research (NWO; grant number 453-14-006 ) and an EU-ENHANCE grant from the European Community's Seventh Framework Programme (FP7; grant number 308438 ). This work was conducted by the World Bank's Disaster Resilience Analytics and Solutions (D-RAS) Knowledge Silo Breaker (KSB), under the Global Practice for Urban, Disaster Risk Management, Resilience and Land (GPURL). This research has been funded by the World Bank and the Global Facility for Disaster Reduction and Recovery ( GFDRR ) grant ( TF0B1140 ).

Publisher Copyright:
© 2022

Keywords

Bayesian updating
Hurricane Dorian
Rapid damage assessment
Social media
UAVs
Vulnerability curves

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10.1016/j.ijdrr.2022.102839

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de Bruijn, J. A., Daniell, J. E., Pomonis, A., Gunasekera, R., Macabuag, J., de Ruiter, M. C., Koopman, S. J., Bloemendaal, N., de Moel, H., & Aerts, J. C. J. H. (2022). Using rapid damage observations for Bayesian updating of hurricane vulnerability functions: A case study of Hurricane Dorian using social media. International Journal of Disaster Risk Reduction, 72, 1-16. Article 102839. https://doi.org/10.1016/j.ijdrr.2022.102839

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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.",

keywords = "Bayesian updating, Hurricane Dorian, Rapid damage assessment, Social media, UAVs, Vulnerability curves",

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

note = "Funding Information: Our research was funded by an NWO-Vici grant from the Netherlands Organisation for Scienti?c Research (NWO; grant number 453-14-006) and an EU-ENHANCE grant from the European Community's Seventh Framework Programme (FP7; grant number 308438). This work was conducted by the World Bank's Disaster Resilience Analytics and Solutions (D-RAS) Knowledge Silo Breaker (KSB), under the Global Practice for Urban, Disaster Risk Management, Resilience and Land (GPURL). This research has been funded by the World Bank and the Global Facility for Disaster Reduction and Recovery (GFDRR) grant (TF0B1140). Funding Information: Our research was funded by an NWO-Vici grant from the Netherlands Organisation for Scientiﬁc Research (NWO; grant number 453-14-006 ) and an EU-ENHANCE grant from the European Community's Seventh Framework Programme (FP7; grant number 308438 ). This work was conducted by the World Bank's Disaster Resilience Analytics and Solutions (D-RAS) Knowledge Silo Breaker (KSB), under the Global Practice for Urban, Disaster Risk Management, Resilience and Land (GPURL). This research has been funded by the World Bank and the Global Facility for Disaster Reduction and Recovery ( GFDRR ) grant ( TF0B1140 ). Publisher Copyright: {\textcopyright} 2022",

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de Bruijn, JA, Daniell, JE, Pomonis, A, Gunasekera, R, Macabuag, J, de Ruiter, MC , Koopman, SJ , Bloemendaal, N , de Moel, H & Aerts, JCJH 2022, 'Using rapid damage observations for Bayesian updating of hurricane vulnerability functions: A case study of Hurricane Dorian using social media', International Journal of Disaster Risk Reduction, vol. 72, 102839, pp. 1-16. https://doi.org/10.1016/j.ijdrr.2022.102839

Using rapid damage observations for Bayesian updating of hurricane vulnerability functions: A case study of Hurricane Dorian using social media. / de Bruijn, Jens A.; Daniell, James E.; Pomonis, Antonios et al.
In: International Journal of Disaster Risk Reduction, Vol. 72, 102839, 01.04.2022, p. 1-16.

Research output: Contribution to Journal › Article › Academic › peer-review

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AU - Daniell, James E.

AU - Pomonis, Antonios

AU - Gunasekera, Rashmin

AU - Macabuag, Joshua

AU - de Ruiter, Marleen C.

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PY - 2022/4/1

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N2 - 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.

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

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