Model predictive control of stormwater basins coupled with real-time data assimilation enhances flood and pollution control under uncertainty.

Smart stormwater systems equipped with real-time controls are transforming urban drainage management by enhancing the flood control and water treatment potential of previously static infrastructure. Real-time control of detention basins, for instance, has been shown to improve contaminant removal by increasing hydraulic retention times while also reducing downstream flood risk. However, to date, few studies have explored optimal real-time control strategies for achieving both water quality and flood control targets. This study advances a new model predictive control (MPC) algorithm for stormwater detention ponds that determines the outlet valve control schedule needed to maximize pollutant removal and minimize flooding using forecasts of the incoming pollutograph and hydrograph. Comparing MPC against three rule-based control strategies, MPC is found to be more effective at balancing between multiple competing control objectives such as preventing overflows, reducing peak discharges, and improving water quality. Moreover, when paired with an online data assimilation scheme based on Extended Kalman Filtering (EKF), MPC is found to be robust to uncertainty in both pollutograph forecasts and water quality measurements. By providing an integrated control strategy that optimizes both water quality and quantity goals while remaining robust to uncertainty in hydrologic and pollutant dynamics, this study paves the way for real-world smart stormwater systems that will achieve improved flood and nonpoint source pollution management.