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.

中文翻译：

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使用来自社交媒体的快速破坏观测数据进行飓风脆弱性功能的贝叶斯更新：以多里安飓风为例

摘要。灾难发生后立即进行快速影响评估，对于快速有效地调动资源用于应对和恢复工作至关重要。这些评估通常是通过分析风险的三个组成部分来进行的：危害，暴露和脆弱性。漏洞曲线通常使用历史保险数据或专家判断来构建，从而降低了其对特定危害和建筑存量特性的适用性。因此，本文概述了一种创建特定于事件的漏洞曲线的方法，该方法使用贝叶斯统计数据（即零膨胀的beta分布）来更新已存在的漏洞曲线（即先前的漏洞），并获得观察到的影响数据来自社交媒体。该方法在2019年9月袭击巴哈马的多利安飓风的案例研究中得到了应用。我们分析了灾难发生后的前10天主要发布在YouTube上的无人机和其他机载车辆拍摄的镜头。由于其贝叶斯性质，可以使用该方法，而无需考虑可用的数据量，因为它平衡了先验和观测的贡献。