Identifying critical elements in drinking water distribution networks using graph theory

Abstract

Drinking water distribution networks (WDNs) are a crucial infrastructure for life in cities. Deterioration of this ageing, and partly hidden from view, infrastructure can result in losses due to leakage and an increased contamination risk. To counteract this, maintenance strategies are required to maintain the service level. Information on the most critical elements of a WDN, with respect to the functioning of the system as a whole, is essential for prioritising maintenance or rehabilitation activities. In this study a Graph theory based method is developed and applied for efficiently identifying the most critical elements. The main advantage of this method is that it avoids the need to perform elaborate hydrodynamic model calculations. Instead, the structure of the network is the main starting point. The results show that the structure of the network is more decisive than the hydraulics with respect to the criticality of the system's performance as a whole. Results depict that the suggested approach is applicable not only to the main (primary) network, but also to the capillaries which are normally beyond the scope of the traditional methods applied so-far because of the complexity of the networks and the required calculation time.

Optimization of Collective Irrigation Network Layout through the Application of the Analytic Hierarchy Process (AHP) Multicriteria Analysis Method

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Machine Learning‐Based Surrogate Modeling for Urban Water Networks: Review and Future Research Directions

“…Given their relational inductive bias, the newly developed GNN appears as a suitable DL architecture for applications in UWNs, since the natural structure of these systems can be represented using a graph. Researchers have already exploited graph theoretical concepts of UWNs (Deuerlein, 2008;Herrera et al, 2016;Meijer et al, 2018Meijer et al, , 2020. Furthermore, some applications of GNNs in UWNs already exist.…”

Potential of space science technology for water infrastructure management

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