Integrating Model Predictive Control With Stormwater System Design: A Cost‐Effective Method of Urban Flood Risk Mitigation During Heavy Rainfall

Abstract

AbstractThe integration of green‐gray infrastructures with advanced control approaches is revolutionizing the stormwater system retrofitting, emerging as an innovative strategy to mitigate urban flood risks. However, a major challenge lies in balancing the substantial investments of these infrastructure projects with their environmental benefits, such as reduced flooding volume and lower peak flow. Model predictive control (MPC), a dynamic and intelligent control approach, optimizes these environmental benefits but is underutilized in the system design phase for cost‐effectiveness analysis. This study introduces a multi‐scenario model framework that incorporates MPC and other control approaches into stormwater system designs, including the implementation of controlled storage tanks and green infrastructures. This framework provides comprehensive modeling tools for practitioners to evaluate the flood control benefits and costs across various infrastructure designs and control scenarios, ultimately identifying solutions that are both environmentally and economically viable. A case study conducted in a small urban catchment area in Shenzhen City, China, demonstrates the effectiveness of this framework. The results indicate that MPC outperforms other control scenarios, particularly under heavy or extreme rainfall conditions. Notably, MPC not only provides superior environmental benefits but also yields considerable cost savings, ranging from 1,787 to 9,371 USD per hectare compared to static control, equating to a 5% reduction in cost relative to rule‐based control. Such findings suggest that integrating MPC is a cost‐effective alternative to extensive infrastructure expansion for flood management, which significantly enhances the benefit contribution of controlled infrastructures without substantial additional expenses.