Quantifying Community Resilience Using Hierarchical Bayesian Kernel Methods: A Case Study on Recovery from Power Outages
Quantifying Community Resilience Using Hierarchical Bayesian Kernel Methods: A Case Study on Recovery from Power Outages
复制标题
使用分层贝叶斯核方法量化社区复原力：断电恢复案例研究
DOI：
10.1111/risa.13343
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发表时间：
2019
期刊：
影响因子：
3.8
通讯作者：
Baroud, Hiba
中科院分区：
文献类型：
--
作者：
Yu, Jin‐Zhu;Baroud, Hiba
准确衡量受灾害影响的基础设施系统和社区的恢复速度的能力对于确保在中断之前、期间和之后的有效响应和资源分配至关重要。然而，量化这些措施的一个挑战在于缺乏数据，因为社区恢复信息很少记录。为了提供准确的社区恢复措施，一个层次贝叶斯核模型（HBKM）的开发，以预测在风暴期间经历停电的社区的恢复率。使用交叉验证来评估所提出方法的性能，并与两个模型（分层贝叶斯回归模型和Poisson广义线性模型）进行比较。以田纳西州谢尔比县2007年至2017年强风暴后的社区恢复为例，说明了所提出的方法。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准确度。这种方法可以帮助评估数据稀缺时社区的可恢复性，并在灾难发生后为决策提供信息。本文提供了一个说明性示例，展示了社区复原力的准确测量如何有助于降低基础设施恢复的成本。
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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