Model-Predicted Control System for the Real-Time Operation of an Urban Drainage System to Mitigate Urban Flood Risk: A Case Study in the Liede River Catchment, Guangzhou, China

Abstract

A model-predicted control (MPC) system, which is based on a storm water management model (SWMM) and uses a multi-objective particle swarm optimization algorithm, is developed and applied to optimize the real-time operation of an urban drainage system (UDS) in the Liede River catchment, Guangzhou, China. By comparing the results of three control scenarios (i.e., the original control scenario, the current MPC, and the ideal MPC) under three typical rainfall events, the results demonstrate that the MPC system can effectively mitigate urban flood risk in engineering applications and the decision-making of the MPC system is valid. By comparing the control results of the MPC system under different rainfall return periods (e.g., 1, 2, 3, 5, and 10 years), it is found that compared with the original control scenario, the total overflow is reduced by 10%, the total overflow time is reduced by 10%, or the node overflow start time is delayed by an average of 10 minutes, and the real-time control of the MPC system is only effective when the return period of the rainfall is less than three years. It is important to explore different ways of combining the MPC system and feasible capital measures to cope with urban flood risk and challenges of climate change in future works (e.g., mean sea level rise and intense rainfall).