Using rapid damage observations from social media for Bayesian updating of hurricane vulnerability functions: A case study of Hurricane Dorian
Using rapid damage observations from social media for Bayesian updating of hurricane vulnerability functions: A case study of Hurricane Dorian
复制标题
利用社交媒体的快速损害观察对飓风脆弱性函数进行贝叶斯更新：多里安飓风的案例研究
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
J. Aerts
中科院分区：
文献类型：
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作者：
Jens A. de Bruijn;J. Daniell;A. Pomonis;R. Gunasekera;J. Macabuag;Marleen C. de Ruiter;S. J. Koopman;N. Bloemendaal;H. de Moel;J. Aerts
抽象的。灾后立即进行快速影响评估，对于迅速有效地调动资源开展应对和恢复工作至关重要。这些评估通常通过分析风险的三个组成部分进行：危害、暴露和脆弱性。脆弱性曲线往往是利用历史保险数据或专家判断绘制的，这就降低了它们对特定危险和建筑物的特性的适用性。因此，本文概述了一种使用贝叶斯统计（即，0 - 1膨胀β分布）来更新预先存在的脆弱性曲线（即，先前）与来自社交媒体的观察到的影响数据。该方法应用于2019年9月袭击巴哈马群岛的飓风多利安的案例研究。我们分析了在灾难发生后的头10天内发布在YouTube上的主要由无人机（UAV）和其他空中车辆拍摄的镜头。由于其贝叶斯性质，无论可用数据量如何，都可以使用该方法，因为它平衡了先验和观测的贡献。
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.