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

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 commu...

Full description

Saved in:
Bibliographic Details
Published in:Risk analysis 2019-09, Vol.39 (9), p.1930-1948
Main Authors: Yu, Jin‐Zhu, Baroud, Hiba
Format: Article
Language:English
Subjects:
Aftermath
Bayesian analysis
Case studies
Community
Community resilience
Data recovery
Decision making
Disaster management
Disaster relief
Disruption
Electric power
Generalized linear models
hierarchical Bayesian kernel model
Infrastructure
Kernels
Linear analysis
Mathematical models
Model accuracy
Outages
Power
Power failures
power outage
predictive accuracy
Recoverability
Recovery
Regression analysis
Regression models
Resilience
Resource allocation
Restoration
Risk assessment
Statistical models
stochastic dominance
Storms
System effectiveness
Validity
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0272-4332
1539-6924
DOI:10.1111/risa.13343