DOAJ Open Access 2025

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

Zhenyu Huang Yiming Wang Xin Dong Wei Li Yangbo Tang +1 lainnya

Abstrak

Abstract 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.

Topik & Kata Kunci

Water supply for domestic and industrial purposes

Penulis (6)

Zhenyu Huang

Yiming Wang

Xin Dong

Wei Li

Yangbo Tang

Dazhen Zhang

Format Sitasi

APA MLA BibTeX

Huang, Z., Wang, Y., Dong, X., Li, W., Tang, Y., Zhang, D. (2025). Pollution-based integrated real-time control for urban drainage systems: a multi-agent deep reinforcement learning approach. https://doi.org/10.1038/s41545-025-00512-z

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Informasi Jurnal

Tahun Terbit: 2025
Sumber Database: DOAJ
DOI: 10.1038/s41545-025-00512-z
Akses: Open Access ✓