Parameter estimation of Muskingum model using grey wolf optimizer algorithm.

Flood routing plays a crucial role in prevention of major economic and human losses, which, in this study, has been conducted via both three- and four-constant parameter non-linear Muskingum models for four hydrographs, along with the Grey Wolf Optimizer (GWO) algorithm. Three benchmark examples and a real example (Karun river) were investigated. The routing results of the Karun River revealed that in the estimation of the hydrological parameters using the GWO technique, SSQ became 59294 cms in the three-parameter model, compared to the genetic, artificial bee colony (ABC), simulated annealing (SA) and shuffled frog leaping (SFLA) algorithms, decreasing by 68%, 67%, 56% and 55% in comparison with the best modelings performed. As for the four-parameter model, the amount of reduction was 18% with respect to the particle swarm optimization algorithm.•The flood routing is carried out by two non-linear Muskingum model.•The main purpose of this work is to make a comprehensive study between models optimized by AGWO, GWO and other meta-heuristic algorithms.•In order to compare the results of the GWO algorithm to those of more recent algorithms, the flood routing was performed by using the Augmented Grey Wolf Optimizer algorithm as well. © 2021 Published by Elsevier B.V.

Citation

Reyhaneh Akbari, Masoud-Reza Hessami-Kermani. Parameter estimation of Muskingum model using grey wolf optimizer algorithm. MethodsX. 2021;8:101589

PMID: 35004221

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