Graph-Theoretic Surrogate Measure to Analyze Reliability of Water Distribution System Using Bayesian Belief Network–Based Data Fusion Technique

Abstract

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.