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<p>Leak detection in aging water distribution networks (WDNs) is a complex engineering challenge influenced by nonlinear hydraulic performance, infrastructure deterioration, spatial heterogeneity, and limited data on confirmed failures. Purely sensor-based and data-driven approaches often face scalability constraints and rely on simplified assumptions, limiting robustness under real operating conditions. This study proposes a hybrid spatial predictive framework that integrates pressure-driven hydraulic simulation, GIS-based Fuzzy Analytic Hierarchy Process (FAHP), and supervised machine learning to identify leakage-prone nodes in large-scale WDNs. A pressure-driven EPANET model incorporating emitter coefficients and pipe aging effects simulates realistic leakage under diurnal demand patterns. A four-rule screening process identifies 4,699 high-risk nodes from over 39,000 nodes, followed by sensitivity–correlation analysis to determine influential nodes for efficient sensor placement. Spatial and infrastructural characteristics are quantified using Fuzzy AHP through a weighted evaluation of six criteria, with pipe age identified as the dominant factor (0.382). In addition, GIS-based fuzzy AHP enables the development of a spatial leakage-risk map, indicating that more than 30% of the network falls within high (20.98%) and very high (11.06%) risk categories, reflecting significant vulnerability concentrated in aging infrastructure. These features are used to train multiple classifiers, among which Extreme Gradient Boosting (XGBoost) achieves the best performance (Accuracy = 0.961; ROC–AUC = 0.989). Application to a full-scale urban WDN in southern Ontario demonstrates that the proposed framework can support simulation-based prioritization of leakage-prone locations and provide a scalable decision-support tool for data-driven infrastructure management, subject to further validation using field-confirmed leak records.</p>

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Aging water distribution networks: a hybrid spatial decision support and machine learning framework for leak detection

  • 期刊论文

  • 发表日期：

    2026年7月21日

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  • Amir Noori,
  • Ehsan Roshani,
  • Hossein Bonakdari

摘要

Leak detection in aging water distribution networks (WDNs) is a complex engineering challenge influenced by nonlinear hydraulic performance, infrastructure deterioration, spatial heterogeneity, and limited data on confirmed failures. Purely sensor-based and data-driven approaches often face scalability constraints and rely on simplified assumptions, limiting robustness under real operating conditions. This study proposes a hybrid spatial predictive framework that integrates pressure-driven hydraulic simulation, GIS-based Fuzzy Analytic Hierarchy Process (FAHP), and supervised machine learning to identify leakage-prone nodes in large-scale WDNs. A pressure-driven EPANET model incorporating emitter coefficients and pipe aging effects simulates realistic leakage under diurnal demand patterns. A four-rule screening process identifies 4,699 high-risk nodes from over 39,000 nodes, followed by sensitivity–correlation analysis to determine influential nodes for efficient sensor placement. Spatial and infrastructural characteristics are quantified using Fuzzy AHP through a weighted evaluation of six criteria, with pipe age identified as the dominant factor (0.382). In addition, GIS-based fuzzy AHP enables the development of a spatial leakage-risk map, indicating that more than 30% of the network falls within high (20.98%) and very high (11.06%) risk categories, reflecting significant vulnerability concentrated in aging infrastructure. These features are used to train multiple classifiers, among which Extreme Gradient Boosting (XGBoost) achieves the best performance (Accuracy = 0.961; ROC–AUC = 0.989). Application to a full-scale urban WDN in southern Ontario demonstrates that the proposed framework can support simulation-based prioritization of leakage-prone locations and provide a scalable decision-support tool for data-driven infrastructure management, subject to further validation using field-confirmed leak records.