A bayesian approach for estimating the post-earthquake recovery trajectories of electric power systems in Japan
A bayesian approach for estimating the post-earthquake recovery trajectories of electric power systems in Japan
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DOI：
10.1080/23789689.2024.2303801
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发表时间：
2024-01
影响因子：
5.9
通讯作者：
Yuki Handa;Eyitayo A. Opabola;Carmine Galasso
中科院分区：
文献类型：
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作者：
Yuki Handa;Eyitayo A. Opabola;Carmine Galasso
工程系统灾后恢复建模已成为自然灾害灾害风险建模和管理的一个重要方面。民用基础设施系统的灾后恢复轨迹可以使用以下来量化：（a）初始灾后功能水平Q o ;（B）快速性h（即，功能恢复速率）;和（c）恢复时间，Rt。本研究使用贝叶斯估计方法，推导出一组概率模型，估计Q o，Rt，和h的电力网络（EPN）使用震后恢复数据，从2003年至2022年在日本的16个大地震。所考虑的预测（解释）变量包括地震震级，发生年份，地震烈度和暴露人口（PEX）。除了是一个简单而有效的独立工具，拟议的数据驱动模型可以是一个有用的基准工具，模拟为基础的方法EPN恢复建模。
Post-disaster recovery modelling of engineering systems has become an important facet of catastrophe risk modelling and management for natural hazards. The post-disaster recovery trajectory of a civil infrastructure system can be quantified using (a) the initial post-disaster functionality level, Q o ; (b) rapidity, h (i.e., the rate of functionality restoration); and (c) recovery time, R t . This study uses a Bayesian estimation approach to derive a set of probabilistic models to estimate Q o , R t , and h of electric power networks (EPNs) using post-earthquake recovery data from 16 large earthquakes in Japan between 2003 and 2022. The considered predictor (explanatory) variables include earthquake magnitude, year of occurrence, seismic intensity, and exposed population (PEX). Apart from being a simple and efficient stand-alone tool, the proposed data-driven models can be a useful benchmarking tool for simulation-based approaches for EPN recovery modelling.