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

Saeid Khalifeh; Kazem Esmaili; SaeedReza Khodashenas; Saeid Akbarifard

doi:10.1016/j.dib.2020.105398

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

Data Brief. 2020 Mar 10:30:105398. doi: 10.1016/j.dib.2020.105398. eCollection 2020 Jun.

Authors

Saeid Khalifeh¹, Kazem Esmaili², SaeedReza Khodashenas², Saeid Akbarifard³

Affiliations

¹ Water Science and Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran.
² Department of Water Science and Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran.
³ Water Resources Engineering, Department of Hydrology and Water Resources, Faculty of Water Sciences Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.

Abstract

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.

Keywords: Grasshopper optimization algorithm; Kardeh; Muskingum; Non-linear.