Flood mitigation in coastal urban catchments using real-time stormwater infrastructure control and reinforcement learning

• Published in: Journal of hydroinformatics Vol. 23; no. 3; pp. 529 - 547
• Main Authors: Bowes, Benjamin D., Tavakoli, Arash, Wang, Cheng, Heydarian, Arsalan, Behl, Madhur, Beling, Peter A., Goodall, Jonathan L.
• Format: Journal Article
• Language: English
• Published: London IWA Publishing 01.05.2021
• Subjects: Automatic control; Catchments; Climate change; Control systems; Controllability; Environmental risk; Flooding; Floods; Genetic algorithms; Infrastructure; Mitigation; Performance enhancement; Physics; Policies; Ponds; Predictive control; Rain; Real time; real-time control; Reinforcement; reinforcement learning; Retention basins; Run time (computers); Sea level changes; smart stormwater systems; Stormwater; Stormwater management; Strategy; Urban catchments; urban flooding; Valves; Watersheds
• ISSN: 1464-7141; 1465-1734
• DOI: 10.2166/hydro.2020.080

Flooding in coastal cities is increasing due to climate change and sea-level rise, stressing the traditional stormwater systems these communities rely on. Automated real-time control (RTC) of these systems can improve performance, and creating control policies for smart stormwater systems is an active area of study. This research explores reinforcement learning (RL) to create control policies to mitigate flood risk. RL is trained using a model of hypothetical urban catchments with a tidal boundary and two retention ponds with controllable valves. RL's performance is compared to the passive system, a model predictive control (MPC) strategy, and a rule-based control strategy (RBC). RL learns to proactively manage pond levels using current and forecast conditions and reduced flooding by 32% over the passive system. Compared to the MPC approach using a physics-based model and genetic algorithm, RL achieved nearly the same flood reduction, just 3% less than MPC, with a significant 88× speedup in runtime. Compared to RBC, RL was able to quickly learn similar control strategies and reduced flooding by an additional 19%. This research demonstrates that RL can effectively control a simple system and offers a computationally efficient method that could scale to RTC of more complex stormwater systems.