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...

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Autores principales:	Reyhaneh Akbari, Masoud-Reza Hessami-Kermani
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Augmented grey wolf optimizer Flood routing Meta-heuristic algorithms Non-linear Muskingum model Science Q
Acceso en línea:	https://doaj.org/article/c5ad4fc262114ca887886731596d5f48
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spelling	oai:doaj.org-article:c5ad4fc262114ca887886731596d5f482021-12-02T05:01:45ZParameter estimation of Muskingum model using grey wolf optimizer algorithm2215-016110.1016/j.mex.2021.101589https://doaj.org/article/c5ad4fc262114ca887886731596d5f482021-01-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2215016121003794https://doaj.org/toc/2215-0161Flood 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.Reyhaneh AkbariMasoud-Reza Hessami-KermaniElsevierarticleAugmented grey wolf optimizerFlood routingMeta-heuristic algorithmsNon-linear Muskingum modelScienceQENMethodsX, Vol 8, Iss , Pp 101589- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Augmented grey wolf optimizer Flood routing Meta-heuristic algorithms Non-linear Muskingum model Science Q
spellingShingle	Augmented grey wolf optimizer Flood routing Meta-heuristic algorithms Non-linear Muskingum model Science Q Reyhaneh Akbari Masoud-Reza Hessami-Kermani Parameter estimation of Muskingum model using grey wolf optimizer algorithm
description	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.
format	article
author	Reyhaneh Akbari Masoud-Reza Hessami-Kermani
author_facet	Reyhaneh Akbari Masoud-Reza Hessami-Kermani
author_sort	Reyhaneh Akbari
title	Parameter estimation of Muskingum model using grey wolf optimizer algorithm
title_short	Parameter estimation of Muskingum model using grey wolf optimizer algorithm
title_full	Parameter estimation of Muskingum model using grey wolf optimizer algorithm
title_fullStr	Parameter estimation of Muskingum model using grey wolf optimizer algorithm
title_full_unstemmed	Parameter estimation of Muskingum model using grey wolf optimizer algorithm
title_sort	parameter estimation of muskingum model using grey wolf optimizer algorithm
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/c5ad4fc262114ca887886731596d5f48
work_keys_str_mv	AT reyhanehakbari parameterestimationofmuskingummodelusinggreywolfoptimizeralgorithm AT masoudrezahessamikermani parameterestimationofmuskingummodelusinggreywolfoptimizeralgorithm
_version_	1718400795355906048

Parameter estimation of Muskingum model using grey wolf optimizer algorithm

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