Intelligent Urban Flood Management Using Real-Time Forecasting, Multi-Objective Optimization, and Adaptive Pump Operation - Li-Chiu Chang; Ming-Ting Yang; Jia-Yi Liou; Pu-Yun Kow; Fi-John Chang (Smart Cities)
Climate-induced extreme rainfall events are increasing the intensity and frequency of flash floods, highlighting the urgent need for advanced flood management systems in climate-resilient cities. This study introduces an Intelligent Flood Control Decision Support System (IFCDSS), a novel AI-driven solution for real-time flood forecasting and automated pump operations. The IFCDSS integrates multiple advanced tools: machine learning for rapid short-term water level forecasting, NSGA-III for multi-objective optimization, the TOPSIS for robust multi-criteria decision-making, and the ANFIS for real-time pump control. Implemented in the flood-prone Zhongshan Pumping Station catchment in Taipei, the IFCDSS leveraged real-time sensor data to deliver accurate water level forecasts within five seconds for the next 10–30 min, enabling proactive and informed operational responses. Performance evaluations confirm the system’s scientific soundness and practical utility. Specifically, the ANFIS achieved strong accuracy (R2 = 0.81), with most of the prediction errors being limited to a single pump unit. While the conventional manual operations slightly outperformed the IFCDSS in minimizing flood peaks—due to their singular focus—the IFCDSS excelled in balancing multiple objectives: flood mitigation, energy efficiency, and operational reliability. By simultaneously addressing these dimensions, the IFCDSS provides a robust and adaptable framework for urban environments. This study highlights the transformative potential of intelligent flood control to enhance urban resilience and promote sustainable, climate-adaptive development.