StocHastIc Early Leakage Detection System (SHIELDS) for Water Distribution Networks

Jose-Luis Molina (), Carmen Patino-Alonso (), Xi Wan () and Raziyeh Farmani ()
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Jose-Luis Molina: Salamanca University, High Polytechnic School of Engineering Avila
Carmen Patino-Alonso: Salamanca University
Xi Wan: University of Exeter
Raziyeh Farmani: University of Exeter

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2025, vol. 39, issue 8, No 30, 4189-4204

Abstract: Abstract Effective leak detection in water distribution networks is essential to minimize water loss, reduce maintenance costs, and ensure sustainable resource management. This study introduces the Stochastic Early Leakage Detection System (SHIELDS), an innovative methodology employing Bayesian Networks (BNs) for probabilistic leak identification and localization. SHIELDS models a 10-node hydraulic network, selected for its balance between computational efficiency and system complexity, with nodes representing critical points where key variables (pressure, flow rate, velocity, elevation, head loss, and demand) are monitored at five-minute intervals over 31 days. A baseline dataset representing standard operational conditions was established. Synthetic leaks were then introduced at random points to analyze variations in flow, pressure, and velocity as leak indicators. SHIELDS accurately detected leaks, identifying a flow reduction of 13.13 l/s at minute 120. Under maximum demand, lower system pressure reduced leak volumes, with a flow reduction of 17.37 l/s, reflecting the pressure-dependent nature of leaks. The system also captured how leak effects propagate downstream, impacting unaffected nodes. By integrating spatial and temporal dimensions, SHIELDS supports real-time leak detection and proactive strategies. This approach advances artificial intelligence and machine learning applications in water management, providing a robust framework for efficient leak detection and mitigation.

Keywords: AI tools; EWS; Hydraulics; Leakage; Machine learning; Pressure pipe (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11269-025-04158-x

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