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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-01-01
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| Series: | MethodsX |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215016121003794 |
| _version_ | 1818969359258746880 |
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| author | Reyhaneh Akbari Masoud-Reza Hessami-Kermani |
| author_facet | Reyhaneh Akbari Masoud-Reza Hessami-Kermani |
| author_sort | Reyhaneh Akbari |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-20T14:19:20Z |
| format | Article |
| id | doaj.art-c5ad4fc262114ca887886731596d5f48 |
| institution | Directory Open Access Journal |
| issn | 2215-0161 |
| language | English |
| last_indexed | 2024-12-20T14:19:20Z |
| publishDate | 2021-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | MethodsX |
| spelling | doaj.art-c5ad4fc262114ca887886731596d5f482022-12-21T19:37:58ZengElsevierMethodsX2215-01612021-01-018101589Parameter estimation of Muskingum model using grey wolf optimizer algorithmReyhaneh Akbari0Masoud-Reza Hessami-Kermani1Corresponding author.; Department of Civil Engineering, Shahid Bahonar University of Kerman, Kerman, IranDepartment of Civil Engineering, Shahid Bahonar University of Kerman, Kerman, IranFlood 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.http://www.sciencedirect.com/science/article/pii/S2215016121003794Augmented grey wolf optimizerFlood routingMeta-heuristic algorithmsNon-linear Muskingum model |
| spellingShingle | Reyhaneh Akbari Masoud-Reza Hessami-Kermani Parameter estimation of Muskingum model using grey wolf optimizer algorithm MethodsX Augmented grey wolf optimizer Flood routing Meta-heuristic algorithms Non-linear Muskingum model |
| title | 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_short | Parameter estimation of Muskingum model using grey wolf optimizer algorithm |
| title_sort | parameter estimation of muskingum model using grey wolf optimizer algorithm |
| topic | Augmented grey wolf optimizer Flood routing Meta-heuristic algorithms Non-linear Muskingum model |
| url | http://www.sciencedirect.com/science/article/pii/S2215016121003794 |
| work_keys_str_mv | AT reyhanehakbari parameterestimationofmuskingummodelusinggreywolfoptimizeralgorithm AT masoudrezahessamikermani parameterestimationofmuskingummodelusinggreywolfoptimizeralgorithm |