This paper compares the well-known genetic algorithm (GA) and pattern search (PS) optimization methods for forecasting optimal flow releases in a multi-storage system for flood control. The simulation models used by the optimization models include (a) a batch of scripts for data acquisition of forecasted precipitation and their automated post-processing; (b) a hydrological model for rainfall-runoff conversion, and (c) a hydraulic model for simulating river inundation. This paper focuses on (1) demonstrating the application of the framework by applying it to the operation of a hypothetical eight-wetland system in the Cypress Creek watershed in Houston, Texas; and (2) comparing and discussing the performance of the two optimization methods under consideration. The results show that the GA and PS optimal solutions are very similar; however, the computational time required by PS is significantly shorter than that required by GA. The results also show that optimal dynamic water management can significantly mitigate flooding compared to the case without management.

本文比较了著名的遗传算法 (GA) 和模式搜索 (PS) 优化方法，用于预测防洪多蓄水系统中的最佳泄洪量。优化模型使用的模拟模型包括 (a) 一批用于预测降水数据采集及其自动后处理的脚本；(b) 降雨-径流转换的水文模型，以及 (c) 模拟河流淹没的水力模型。本文侧重于 (1) 通过将框架应用于德克萨斯州休斯顿赛普拉斯溪流域中假设的八湿地系统的运行来展示该框架的应用；(2) 比较和讨论所考虑的两种优化方法的性能。结果表明，GA和PS的最优解非常相似；然而，PS所需的计算时间明显短于GA所需的时间。结果还表明，与没有管理的情况相比，最佳动态水管理可以显着减轻洪水。