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

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Summary

Accurately measuring community recovery after disasters is crucial. A new hierarchical Bayesian kernel model (HBKM) effectively predicts recovery rates from power outages, even with limited data, aiding disaster response.

Keywords:
Community resiliencehierarchical Bayesian kernel modelpower outagepredictive accuracystochastic dominance

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Area of Science:

  • Disaster Management
  • Infrastructure Resilience
  • Statistical Modeling

Background:

  • Accurate assessment of community recovery post-disaster is vital for effective response and resource allocation.
  • Limited availability of recorded community recovery data presents a significant challenge in quantifying recovery rates.
  • Understanding community resilience is key to optimizing infrastructure restoration efforts.

Purpose of the Study:

  • To develop a novel method for predicting community recovery rates from power outages following disasters.
  • To address the challenge of data scarcity in measuring community recovery.
  • To enhance decision-making processes for disaster response and infrastructure management.

Main Methods:

  • Development of a hierarchical Bayesian kernel model (HBKM) for predicting community recovery rates.
  • Evaluation of HBKM performance using cross-validation.
  • Comparison of HBKM with hierarchical Bayesian regression and Poisson generalized linear models.
  • Case study in Shelby County, Tennessee, analyzing storm-related power outage recovery (2007-2017).

Main Results:

  • The proposed HBKM demonstrated superior predictive accuracy compared to benchmark models.
  • HBKM achieved the highest average out-of-sample predictive accuracy.
  • Log-likelihood and root mean squared error were used to evaluate predictive accuracy.

Conclusions:

  • The HBKM provides a robust approach for assessing community recoverability, particularly in data-scarce environments.
  • This method can inform critical decision-making for disaster management and infrastructure restoration.
  • Accurate community resilience measures can lead to reduced infrastructure restoration costs.