Građevinar, Vol. 78 No. 09., 2026.
Original scientific paper
https://doi.org/10.14256/JCE.4531.2026
Leak location and magnitude estimation in water distribution networks using an optimised two-stage leak localisation model
Hillary M. Zodape
Shilpa R. Dongre
Abstract
Water loss from urban water distribution networks (WDNs) challenges sustainable water management and utility operations. Early leak detection and accurate localisation are crucial for reducing non-revenue water and maintaining network efficiency. This study proposes a machine learning-based framework integrating EPANET-generated static hydraulic simulations with an artificial neural network (ANN) optimised using particle swarm optimisation (PSO) for effective feature extraction. Pressure data under various leak and no-leak scenarios were simulated, and sensitive sensor nodes were strategically selected to enhance accuracy. By bridging simulation and reality, this method successfully integrates hydraulic modelling with data-driven learning, demonstrating the hybrid framework’s strong potential for scalable and accurate leak detection, localisation, and magnitude estimation in WDNs.
Keywords
water distribution network; hybrid machine learning; PSO; leak localization; leak magnitude
Hrčak ID:
352356
URI
Publication date:
22.10.2026.
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