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<p>Pressure management in aging water distribution networks (WDNs) is often constrained by infrastructural rigidity and the high cost of physical partitioning. Pressure management and leakage monitoring are pivotal for sustainable WDNs. This study proposes a non-invasive Pressure Management Area (PMA) partitioning framework integrating Fuzzy C-Means (FCM) clustering, Genetic Algorithm-optimized Pressure Reducing Valves (PRVs) control, and game theory-enhanced multi-criteria decision-making. The approach uses nodal pressure fluctuation similarity for partition establishment without valve closures. A Nash equilibrium-based combinatorial weighting model harmonizes entropy, CRITIC, and coefficient of variation methods. Sensor placement is optimized via Euclidean distance-based pressure sensitivity analysis. Applied to the Modena network, the four aeras configuration achieved an 18.03% leakage reduction—outperforming spectral clustering (16.6%) and graph partitioning (10.03%)—and reduced high-pressure areas (&gt; 30&#xa0;m head) by over 67%. Sensors placed strategically attained 76.45% leakage detection coverage. The framework eliminates physical pipe isolation by replacing valve closures with optimized PRVs deployment, significantly reducing infrastructure renovation demands while maintaining equivalent leakage control efficacy in aging networks. By bridging multi-criteria decision models with hydraulic optimization, this work advances intelligent, non-invasive solutions for urban water resource management.</p>

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Enhancing Leakage Control in Aging Networks: A Hybrid Game Theory-TOPSIS Approach for Non-invasive Pressure Management

  • 期刊论文

  • 发表日期：

    2026年3月16日

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  • Hongyan Li,
  • Zifeng Chang,
  • Wentao Shi,
  • Feng Zhang,
  • Jingkai Hao

摘要

Pressure management in aging water distribution networks (WDNs) is often constrained by infrastructural rigidity and the high cost of physical partitioning. Pressure management and leakage monitoring are pivotal for sustainable WDNs. This study proposes a non-invasive Pressure Management Area (PMA) partitioning framework integrating Fuzzy C-Means (FCM) clustering, Genetic Algorithm-optimized Pressure Reducing Valves (PRVs) control, and game theory-enhanced multi-criteria decision-making. The approach uses nodal pressure fluctuation similarity for partition establishment without valve closures. A Nash equilibrium-based combinatorial weighting model harmonizes entropy, CRITIC, and coefficient of variation methods. Sensor placement is optimized via Euclidean distance-based pressure sensitivity analysis. Applied to the Modena network, the four aeras configuration achieved an 18.03% leakage reduction—outperforming spectral clustering (16.6%) and graph partitioning (10.03%)—and reduced high-pressure areas (> 30 m head) by over 67%. Sensors placed strategically attained 76.45% leakage detection coverage. The framework eliminates physical pipe isolation by replacing valve closures with optimized PRVs deployment, significantly reducing infrastructure renovation demands while maintaining equivalent leakage control efficacy in aging networks. By bridging multi-criteria decision models with hydraulic optimization, this work advances intelligent, non-invasive solutions for urban water resource management.