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<p>This study presents a multi-agent reinforcement learning (MARL) framework for integrated real-time control (RTC) of urban drainage systems (UDSs), coordinating sewers, wastewater treatment plants (WWTPs), and receiving waters. Trained within a hydraulic–water quality simulation environment using QMIX, the framework enables facility-level decision-making and adaptive system coordination. Applied to Lu’an City, China, MARL achieved a 25.4% reduction in flooding and overflow volumes and an 18.0% decrease in river pollutants relative to benchmark strategies, while maintaining real-time control feasibility (6.35 s per 5-min interval). Under rainfall forecast and sensor noise uncertainty, MARL improved performance stability by 44.7–52.4%. Despite operational trade-offs, the framework supports integrated system optimization and consistent water quality improvements in urban settings.</p>

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Pollution-based integrated real-time control for urban drainage systems: a multi-agent deep reinforcement learning approach

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月21日

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  • Zhenyu Huang,
  • Yiming Wang,
  • Xin Dong,
  • Wei Li,
  • Yangbo Tang,
  • Dazhen Zhang

摘要

This study presents a multi-agent reinforcement learning (MARL) framework for integrated real-time control (RTC) of urban drainage systems (UDSs), coordinating sewers, wastewater treatment plants (WWTPs), and receiving waters. Trained within a hydraulic–water quality simulation environment using QMIX, the framework enables facility-level decision-making and adaptive system coordination. Applied to Lu’an City, China, MARL achieved a 25.4% reduction in flooding and overflow volumes and an 18.0% decrease in river pollutants relative to benchmark strategies, while maintaining real-time control feasibility (6.35 s per 5-min interval). Under rainfall forecast and sensor noise uncertainty, MARL improved performance stability by 44.7–52.4%. Despite operational trade-offs, the framework supports integrated system optimization and consistent water quality improvements in urban settings.