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Quantifying post-disaster business recovery through Bayesian methods
Structure and Infrastructure Engineering ( IF 3.7 ) Pub Date : 2020-06-13 , DOI: 10.1080/15732479.2020.1777569
Mohammad Aghababaei 1 , Maria Koliou 1 , Maria Watson 2 , Yu Xiao 3
Affiliation  

Abstract

Business recovery after a disaster plays an important role in the socioeconomic recovery of a community. This study focuses on the development of a probabilistic modelling approach for quantifying and predicting business recovery through Bayesian linear regression. The proposed modelling approach consists of three steps including data collection, development of model forms, and model selection through rigorous evaluation and elimination steps. Four attributes, namely business cease operation days, revenue recovery, customer retention, and employee retention, which describe the post-disaster recovery state of a business, are considered. One of the main contributions of this study is incorporating the interplay between household and businesses in a community in developing predictive business recovery models. Towards that direction, different methods to account for the effect of household recovery into the customer retention rate of a business are investigated and proposed. As an application, the proposed modelling approach is applied on the results of a longitudinal field study at the community of Lumberton, NC, which was heavily impacted by the 2016 Hurricane Matthew, focusing on business recovery. The predictive models proposed in this study may be further applicable in risk-based resilience assessment of communities following disastrous events.



中文翻译:

通过贝叶斯方法量化灾后业务恢复

摘要

灾难后的业务恢复在社区的社会经济恢复中起着重要作用。这项研究的重点是通过贝叶斯线性回归来量化和预测业务恢复的概率建模方法的发展。拟议的建模方法包括三个步骤,包括数据收集,模型形式的开发以及通过严格评估和消除步骤的模型选择。考虑了四个属性,即停业营业日,收入恢复,客户保留和员工保留,这些属性描述了企业的灾难后恢复状态。这项研究的主要贡献之一是,在开发预测性业务恢复模型时,将社区中家庭与企业之间的相互作用纳入考虑范围。朝那个方向,研究并提出了将家庭恢复的影响纳入企业客户保留率的不同方法。作为应用程序,拟议的建模方法被应用于北卡罗来纳州朗伯顿社区的一项纵向田野研究的结果,该研究受到2016年马修飓风的严重影响,重点是业务恢复。这项研究中提出的预测模型可能会进一步适用于灾难事件后社区的基于风险的复原力评估。专注于业务恢复。这项研究中提出的预测模型可能会进一步适用于灾难事件后社区的基于风险的复原力评估。专注于业务恢复。这项研究中提出的预测模型可能会进一步适用于灾难事件后社区的基于风险的复原力评估。

更新日期:2020-06-13
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