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

机译：使用分层贝叶斯核方法量化社区复原力：以停电恢复为例

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摘要

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.
机译：准确测量受灾难影响的基础架构系统和社区的恢复率的能力对于确保在破坏发生之前，之中和之后进行有效的响应和资源分配至关重要。但是，量化此类措施的挑战在于缺乏数据，因为很少记录社区恢复信息。为了提供准确的社区恢复措施，开发了分级贝叶斯内核模型（HBKM）来预测风暴期间遇到停电的社区的恢复率。使用交叉验证评估了所提出方法的性能，并与分层贝叶斯回归模型和Poisson广义线性模型这两个模型进行了比较。提出了一个案例研究，重点关注田纳西州谢尔比县（Shelby County）在2007年至2017年之间遭受严重风暴后的社区恢复情况，以说明该方法。使用对数似然和均方根误差评估模型的预测准确性。 HBKM平均产生最高的样本外预测精度。当数据稀缺时，这种方法可以帮助评估社区的可恢复性，并在灾难后为决策提供依据。给出了一个说明性示例，演示了社区复原力的准确度量如何有助于降低基础架构恢复成本。

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