Data on optimization of the non-linear Muskingum flood routing in Kardeh River using Goa algorithm

• Main Authors: Saeid Khalifeh, Kazem Esmaili, SaeedReza Khodashenas, Saeid Akbarifard
• Format: Article
• Language: English
• Published: Elsevier 2020-06-01
• Series: Data in Brief
• Subjects: Muskingum; Kardeh; Non-linear; Grasshopper optimization algorithm
• Online Access: http://www.sciencedirect.com/science/article/pii/S2352340920302924

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.