Localization of Multiple Existing Leakages in Water Distribution Networks by a Graph-based Genetic Algorithm

Abstract Submission: Water utility operators prioritize the timely repair of leaks, an activity that refers to the challenging task of spatially localizing of multiple existing leakages within a water distribution network (WDN). In the literature, Genetic Algorithms (GAs) are used to find the best combinations of leakages, typically in combination with a reduced number of candidate leakage nodes to reduce computational effort. To address this limitation, a graph-based GA is proposed in this work considering the topological relationship between the leakage candidate nodes and sizes for the creation of the offspring. In more detail, each gene in the graph-based GA consists of a leakage place and leakage size and two random genes are selected based on the roulette wheel selection for the creation of the offspring. Afterward, possible offspring nodes are selected within the shortest path between these two leakage places or in spatial proximity connected to the shortest path, where the leakage size is set to a random value between the leakage sizes of the two genes. The developed approach is tested on a part of a WDN with 100 different leakage scenarios with varying number of leakages, sizes, and locations. The graph-based GA significantly improves leakage localization compared to a classic GA and linear programming solver, with a median distance of 44 m between suspected leakage places and the actual leakage locations under perfect conditions, while also being computationally efficient. However, the achievable performance is strongly affected by measurement errors, model uncertainties, and partially unknown nodal demands, and is more accurate for localizing leakage places near exactly measured locations and with larger leakage sizes.

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