Dimensions of superiority: How deep reinforcement learning excels in urban drainage system real-time control.

ABSTRACT: Reducing combined sewer overflows and flooding is crucial for the efficient operation of urban drainage systems. Traditional real-time control (RTC) methods often fall short in efficiency and performance, which prompts the exploration of innovative approaches. Deep reinforcement learning (DRL) has recently emerged as a promising technique to enhance RTC performance. This study evaluates the effectiveness of RTC using a multi-agent-based DRL approach. We developed a comprehensive evaluation framework incorporating multiple quantitative indicators, including control objectives, decision time, robustness, and adaptability. To validate our framework, we conducted a case study on an urban drainage system in Suzhou, China, analyzing 31 historical rainfall events. Our findings reveal that DRL can

SUBMITTER: Huang Z 

PROVIDER: S-EPMC11850747 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature