Khalifeh Saeid, Esmaili Kazem, Khodashenas SaeedReza, Akbarifard Saeid
Water Science and Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran.
Department of Water Science and Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran.
Data Brief. 2020 Mar 10;30:105398. doi: 10.1016/j.dib.2020.105398. eCollection 2020 Jun.
This article describes the time series data for optimizing the Non-linear Muskingum flood routing of the Kardeh River, located in Northeastern of Iran for a period of 2 days (from 27 April 1992 to 28 April 1992). The utilized time-series data included river inflow, Storage volume and river outflow. In this data article, a model based on the Grasshopper Optimization Algorithm (GOA) was developed for the optimization of the Non-linear Muskingum flood routing model. The GOA algorithm was compared with other metaheuristic algorithms such as the Genetic Algorithm (GA) and Harmony search (HS). The analysis showed that the best solutions achieved by the GOA, Genetic Algorithm (GA), and Harmony search (HS) were 3.53, 5.29, and 5.69, respectively. The analysis of these datasets revealed that the GOA algorithm was superior to GA and HS algorithms for the optimal flood routing river problem.
本文描述了位于伊朗东北部的卡尔德河为期2天（从1992年4月27日至1992年4月28日）的优化非线性马斯京根洪水演进时间序列数据。所使用的时间序列数据包括河流入流量、蓄水量和河流出流量。在这篇数据文章中，开发了一种基于蚱蜢优化算法（GOA）的模型，用于优化非线性马斯京根洪水演进模型。将GOA算法与其他元启发式算法，如遗传算法（GA）和和声搜索（HS）进行了比较。分析表明，GOA、遗传算法（GA）和和声搜索（HS）获得的最佳解分别为3.53、5.29和5.69。对这些数据集的分析表明，在最优洪水演进河流问题上，GOA算法优于GA和HS算法。
