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Risk analysis : an official publication of the Society for Risk Analysis
09, 2019;39 (9): 1930-1948.

Quantifying Community Resilience Using Hierarchical Bayesian Kernel Methods: A Case Study on Recovery from Power Outages.

Jin-Zhu Yu, Hiba Baroud
Author Information
  1. Jin-Zhu Yu: Department of Civil and Environmental Engineering, Vanderbilt University, Nashville, TN, USA. ORCID
  2. Hiba Baroud: Department of Civil and Environmental Engineering, Vanderbilt University, Nashville, TN, USA. ORCID
PMID: 31287575 DOI: 10.1111/risa.13343

Abstract

The ability to accurately measure recovery rate of infrastructure systems and communities impacted by disasters is vital to ensure effective response and resource allocation before, during, and after a disruption. However, a challenge in quantifying such measures resides in the lack of data as community recovery information is seldom recorded. To provide accurate community recovery measures, a hierarchical Bayesian kernel model (HBKM) is developed to predict the recovery rate of communities experiencing power outages during storms. The performance of the proposed method is evaluated using cross-validation and compared with two models, the hierarchical Bayesian regression model and the Poisson generalized linear model. A case study focusing on the recovery of communities in Shelby County, Tennessee after severe storms between 2007 and 2017 is presented to illustrate the proposed approach. The predictive accuracy of the models is evaluated using the log-likelihood and root mean squared error. The HBKM yields on average the highest out-of-sample predictive accuracy. This approach can help assess the recoverability of a community when data are scarce and inform decision making in the aftermath of a disaster. An illustrative example is presented demonstrating how accurate measures of community resilience can help reduce the cost of infrastructure restoration.

Keywords

Community resilience hierarchical Bayesian kernel model power outage predictive accuracy stochastic dominance

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Journal Article Research Support, U.S. Gov't, Non-P.H.S.
Article has an altmetric score of 3

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