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Bayesian modeling of flood control networks for failure cascade characterization and vulnerability assessment

机译：河口勘测级联级联特征及脆弱性评估的模型模型

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Abstract This paper presents a Bayesian network model to assess the vulnerability of the flood control infrastructure and to simulate failure cascade based on the topological structure of flood control networks along with hydrological information gathered from sensors. Two measures are proposed to characterize the flood control network vulnerability and failure cascade: (a) node failure probability (NFP), which determines the failure likelihood of each network component under each scenario of rainfall event, and (b) failure cascade susceptibility, which captures the susceptibility of a network component to failure due to failure of other links. The proposed model was tested in both single watershed and multiple watershed scenarios in Harris County, Texas using historical data from three different flooding events, including Hurricane Harvey in 2017. The proposed model was able to identify the most vulnerable flood control network segments prone to flooding in the face of extreme rainfall. The framework and results furnish a new tool and insights to help decision‐makers to prioritize infrastructure enhancement investments and actions. The proposed Bayesian network modeling framework also enables simulation of failure cascades in flood control infrastructures, and thus could be used for scenario planning as well as near‐real‐time inundation forecasting to inform emergency response planning and operation, and hence improve the flood resilience of urban areas.
机译：摘要本文介绍了贝叶斯网络模型，以评估防洪基础设施的脆弱性，并根据洪水控制网络的拓扑结构以及从传感器收集的水文信息来模拟失效级联。提出了两项​​措施来表征洪水控制网络漏洞和故障级联：（a）节点故障概率（NFP），其在降雨事件的每种情况下确定每个网络组件的失败可能性，（b）故障级联易感性由于其他链路故障，捕获网络组件的易感性。拟议的模型在德克萨斯州哈里斯县的单一流域和多个流域情景中进行了测试，德克萨斯州的历史数据来自三种不同的洪水事件，包括2017年飓风哈维。拟议的模式能够识别最容易受到洪水的泛滥洪水控制网段面对极端的降雨。该框架和结果提供了一种新的工具和见解，帮助决策者优先考虑基础设施增强投资和行动。拟议的贝叶斯网络建模框架还支持洪水控制基础设施的故障级联模拟，因此可用于方案规划以及近乎实时的淹没预测，以告知应急响应规划和操作，从而提高洪水抵御能力城市地区。

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