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With the increasing frequency and severity of disasters resulting especially from natural hazards and impacting both infrastructure systems and communities, thus challenging their timely recovery, there is a strong need to prepare for more effective response and recovery. Communities have especially struggled to understand the aspects of recovery patterns for different systems and prepare accordingly. Therefore, it is essential to develop models that are able to measure and estimate the recovery trajectory for a certain community or infrastructure network given system characteristics and event information. The objective of the study is to deploy the Poisson Bayesian kernel model developed and tested in earlier work in risk analysis to measure the recovery rate of a system. In this paper, the model is implemented and tested on a resilience modeling case study of power systems. The model is validated using a comparison to other count data models such as Poisson generalized linear model and the negative binomial generalized linear model.
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Quantifying Community Resilience Using Hierarchical Bayesian Kernel Methods: A Case Study on Recovery from Power Outages
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.
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- Award ID(s):
- 1635717
- PAR ID:
- 10460078
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Risk Analysis
- Volume:
- 39
- Issue:
- 9
- ISSN:
- 0272-4332
- Format(s):
- Medium: X Size: p. 1930-1948
- Size(s):
- p. 1930-1948
- Sponsoring Org:
- National Science Foundation
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