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

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  • Jin‐Zhu Yu
  • Hiba Baroud

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.

Suggested Citation

  • Jin‐Zhu Yu & Hiba Baroud, 2019. "Quantifying Community Resilience Using Hierarchical Bayesian Kernel Methods: A Case Study on Recovery from Power Outages," Risk Analysis, John Wiley & Sons, vol. 39(9), pages 1930-1948, September.
    • Handle: RePEc:wly:riskan:v:39:y:2019:i:9:p:1930-1948
    DOI: 10.1111/risa.13343
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    4. Moglen, Rachel & Leibowicz, Benjamin D. & Kwasinski, Alexis & Cruse, Grant, 2024. "Optimal restoration of power infrastructure following a disaster with environmental hazards," Socio-Economic Planning Sciences, Elsevier, vol. 95(C).
    5. Yu, Jin-Zhu & Whitman, Mackenzie & Kermanshah, Amirhassan & Baroud, Hiba, 2021. "A hierarchical Bayesian approach for assessing infrastructure networks serviceability under uncertainty: A case study of water distribution systems," Reliability Engineering and System Safety, Elsevier, vol. 215(C).

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