Comparison of the genetic algorithm and pattern search methods for forecasting optimal flow releases in a multi-storage system for flood control
Comparison of the genetic algorithm and pattern search methods for forecasting optimal flow releases in a multi-storage system for flood control
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DOI：
10.1016/j.envsoft.2021.105198
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发表时间：
2021-11
期刊：
影响因子：
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通讯作者：
Arturo S. Leon;Lin-Long Bian;Yun Tang
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文献类型：
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作者：
Arturo S. Leon;Lin-Long Bian;Yun Tang
本文比较了著名的遗传算法（GA）和模式搜索（PS）优化方法预测最佳流量释放在多个存储系统的防洪。优化模型所使用的模拟模型包括：（a）一批用于预测降水量数据采集及其自动化后处理的脚本;（B）一个用于降雨-径流转换的水文模型;以及（c）一个用于模拟河流淹没的水力模型。本文的重点是（1）通过将其应用于德克萨斯州休斯顿柏树溪流域的假设八湿地系统的操作来演示该框架的应用;（2）比较和讨论两种优化方法的性能。结果表明，GA和PS的最优解非常相似，但PS所需的计算时间明显短于GA所需的。结果还表明，最优动态水管理可以显着减轻洪水相比，没有管理的情况下。
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.