AI-based design of urban stormwater detention facilities accounting for carryover storage

Abstract

Rapid urbanization in metropolitan areas easily triggers flashy floods. Urban drainage systems conveying stormwater out of cities are key infrastructure elements for flood mitigation. This study develops an intelligent urban flood drainage system accounting for carryover storage through optimizing the multi-objective operation rules of pumping stations for effectual flood management in Taipei City. The Yu-Cheng pumping station constitutes the study case, and a large number of datasets collected from 17 typhoon/storm events are adopted for model construction and validation. Three objective functions are designed to minimize: (1) the sum of water level fluctuations in the flood storage pond (FSP); (2) the sum of peak FSP water levels; and (3) the mean absolute difference of pump switches between two consecutive times along operation sequence. The non-dominated sorting genetic algorithm II (NSGA-II) is applied to searching the Pareto-optimal solutions that optimize the trade-off between the objectives. We next formulate the optimal operation rules through a two-tier sorting process based on a compromised Pareto-optimal solution. The comparison of the simulated results obtained from both the optimal operation rules and current operation rules indicate that the optimal operation rules outperform current operation rules for all three objectives, with improvement rates reaching 43% (OBJ1), 3% (OBJ2) and 71% (OBJ3), respectively. We demonstrate that the derived intelligent urban flood drainage system can serve as reliable and efficient operational strategies for urban flood management and flood risk mitigation.