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首页> 外文期刊>Journal of Water Resources Planning and Management >Graph-Theoretic Surrogate Measure to Analyze Reliability of Water Distribution System Using Bayesian Belief Network-Based Data Fusion Technique
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Graph-Theoretic Surrogate Measure to Analyze Reliability of Water Distribution System Using Bayesian Belief Network-Based Data Fusion Technique

机译：基于贝叶斯信念网络的数据融合技术的图论替代度量分析配水系统的可靠性

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摘要

Reliability assessment is an integral component of the decision-making process in the planning, design, and operations of water distribution networks (WDNs). Two different approaches are used to evaluate the reliability of WDNs: topological and hydraulic. Operational data and hydraulic layout in normal and abnormal conditions are not usually available to allow the computation of the hydraulic reliability. In this paper, four topological graph metrics (betweenness, topological information centrality, eigenvector centrality, and principal component centrality) were considered. Performance of the four metrics was compared with simulation-based hydraulic reliability. The comparison shows that no single topological graph metrics approach can capture characteristics of the complex networks. Using a Bayesian belief network (BBN)-based data fusion technique, the four topological graph metrics were combined into a single metric. The BBN model allowed embedding of the hydraulic process and capturing the uncertainty related to demand fluctuations and flow pattern changes in the network. The approach is applied to the Richmond case study and the results identify the majority of vulnerable areas defined using the hydraulic model and provide the ranking of the priority of interventions in WDNs. A Spearman rank correlation analysis was undertaken, and a heat map of the different results were generated for visual observation. The result from the data fusion technique has significantly improved accuracy of the topological graph metrics.
机译：在水分配网络（WDN）的规划，设计和运营中，可靠性评估是决策过程中不可或缺的组成部分。两种不同的方法用于评估WDN的可靠性：拓扑和水力。通常无法获得正常和异常状态下的操作数据和液压布局，以进行液压可靠性的计算。在本文中，考虑了四个拓扑图度量（中间性，拓扑信息中心性，特征向量中心性和主成分中心性）。将这四个指标的性能与基于仿真的液压可靠性进行了比较。比较表明，没有单一的拓扑图度量方法可以捕获复杂网络的特征。使用基于贝叶斯信念网络（BBN）的数据融合技术，将四个拓扑图指标合并为一个指标。 BBN模型允许嵌入液压过程并捕获与需求波动和网络中流量模式变化有关的不确定性。该方法应用于里士满案例研究，结果确定了使用水力模型定义的大多数脆弱区域，并提供了WDN干预优先级的排名。进行了Spearman等级相关分析，并生成了不同结果的热图以进行视觉观察。数据融合技术的结果显着提高了拓扑图指标的准确性。

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