Conference Agenda
Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
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Daily Overview |
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SES 3-3-1: Artificial Intelligence machine learning 2 Location: HSB0 Session Chair: Pete Melville-Shreeve Session Chair: Sebastian Ramsauer | |
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2:30pm - 2:45pm
Superiority of Deep Reinforcement Learning in Urban Drainage System Real-time Control 1: School of Environment, Tsinghua University, Beijing, China; 2: Environmental Simulation and Pollution Control State Key Joint Laboratory, Beijing, China Reducing sewer overflows and flooding is vital for urban drainage systems. Traditional real-time control (RTC) methods often lack efficiency, leading to the exploration of new techniques like deep reinforcement learning (DRL). This study assesses RTC effectiveness using a multi-agent DRL approach, with a framework evaluating control objectives, decision time, robustness, and adaptability. A case study in Suzhou, China, involving 31 rainfall events, shows DRL reduces flooding and overflow risks by 15.1% to 43.5% compared to traditional methods. However, this benefit came at the cost of higher energy use (10.3% and 7.7%) and increased pump switches (4.3 and 2.2 times), reflecting a trade-off shaped by the objective weightings (80% environmental, 10% energy, 10% switching). DRL also offers superior efficiency, robustness, and adaptability, highlighting its potential in urban drainage management and infrastructure resilience. | |