A Physics-Guided Optimization Framework Using Deep Learning Surrogates for Multi-Objective Control of Combined Sewer Overflows

Combined sewer overflow (CSO) pollution threatens urban water environments, yet optimizing integrated green–grey infrastructure solutions remains computationally intensive, often making robust, large-scale multi-algorithm comparisons impractical. This study’s primary contribution is the development of an innovative physics-guided optimization framework that overcomes this computational barrier. By coupling a deep learning surrogate (trained on 60,000 scenarios generated in 7.7 h) with evolutionary algorithms, this framework provides a 6.2- to 7.7-fold acceleration in total project time (approximately 13 h vs. 80–100 h) compared to direct SWMM optimization. This significant speedup enabled a comprehensive comparative analysis of four multi-objective evolutionary algorithms (MOEAs), which established NSGA-II’s superiority in discovering a larger and more diverse set of optimal trade-off solutions. The physics-guided surrogate achieved an R2 of 0.9965 and a Mean Absolute Error (MAE) corresponding to 0.5% of the baseline overflow volume. The validated framework successfully identified Permeable Pavement as the dominant control variable and a critical knee-point scenario. This solution, requiring a 426 million CNY investment, achieved a 67.0% overflow volume reduction and a 74.4% COD load reduction under the 5-year design storm. Furthermore, the optimized system demonstrated high resilience to extreme events, contrasting sharply with the failure of a cost-minimized approach, which underscores the importance of designing for resilience. This framework provides urban planners with a validated, efficient, and reliable methodology for designing resilient, cost-effective CSO control systems.