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A Hydrodynamic Model and Data-Driven Evolutionary Multi-Objective Optimization Algorithm Based Optimal Operation Method for Multi-barrage Flood Control
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DOI：10.1007/s11269-024-03867-z.png)
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Abstract
En 中文
The flood control operation of river barrages represents a multi-objective optimization problem with conflicting decision objectives, introducing risks into the decision-making process. Most existing optimization methods for operational rule sets encounter challenges related to the insufficient representation of flood accuracy and prolonged computational duration. Considering these two issues, this study aimed to propose a novel multi-objective barrage optimization operation approach based on a hydrodynamic model and a data-driven evolutionary algorithm. This approach employs a hydrodynamic model to precisely simulate the flood propagation process and provide the required hydraulic characteristics. Utilizing the results provided by the hydrodynamic model as foundational data, a multi-objective particle swarm algorithm was employed to drive the search for Pareto-optimal operational rules. Subsequently, the Kriging model is integrated into the optimization process, wherein only potential nondominated solutions in the offspring population were selected for exact objective function evaluations. This significantly reduced the frequency of calls to the hydrodynamic model, thereby enhancing the efficiency of optimization computations. The proposed approach was applied to a real multi-barrage flood control system for the rivers in the urban city of Chengdu, China. The results indicate that this method can optimize and solve the multi-objective operational rules for barrage flood control with limited computational resources. The obtained Pareto-optimal operational rules also illustrate the trade-off relationships among multiple objectives, suggesting that it is possible to mitigate downstream flood risks at the cost of increasing upstream flood risks, and vice versa. The new method can provide precise guidance for flood control scheduling of barrages during the flood season, enabling decision makers to choose the operation rules according to their own risk preferences.
Keywords:
Flood control
Flood risk mitigation
Barrage operation rules
Multi-objective optimization
Data-driven
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