Efficient urban flood control and drainage management framework based on digital twin technology and optimization scheduling algorithm

Abstract

Urban flood control and drainage systems often face significant challenges in coordinating municipal drainage with river-lake flood prevention during flood seasons. Rising river levels can create backwater effects, which substantially increase urban flood risks. Traditional water management approaches are limited by delayed monitoring data updates, slow flood forecasting processes, and inadequate decision support, making it difficult to address the complex, multi-objective demands of flood control. These limitations exacerbate flooding threats and hamper effective urban flood management. To address these challenges, a digital twin experimental platform for river and lake water systems was developed to enhance the comprehensive management of urban flood control and drainage. The platform integrates an engineering entity, a backend system, and a digital twin component. Real-time data acquisition and virtual-real interactions between physical facilities and the digital twin were achieved using Programmable Logic Controller (PLC) technology, while the Unity3D engine enabled advanced visualization and data rendering. Furthermore, a novel model incorporating deep learning and a multi-objective optimization algorithm was proposed to optimize drainage pump scheduling rules. A comparative analysis was conducted to evaluate flood risks and operation and maintenance costs before and after optimization. The results demonstrated that the platform was well-designed for comprehensive flood protection and drainage management. The NSE coefficients for river and lake water levels exceeded 95.18 %, and the relative error in pump operation times remained below 4.11 % across various scenarios involving river inflows and drainage operations. The backwater effect at drainage outlets was primarily driven by river flow and downstream lake levels. The optimization strategy effectively balanced water level control and operational objectives, reducing water level targets by 24.99 %, 40.36 %, and 51.61 % under different scenarios. This framework not only offers innovative solutions for urban flood management but also provides strong technical support for optimizing flood control and drainage system operations.