Application of Whale Optimization Algorithm Combined with Adaptive Neuro-Fuzzy Inference System for Estimating Suspended Sediment Load

In Iran, no detailed information on the amount of erosion, sediment transport, and sedimentation of rivers, and in many cases, there are many differences between measurements. Since the flow regime and consequently the sediment regime in the drainage basins are not constant, estimation of sediment d...

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Autores principales:	Hojjat Emami, Somayeh Emami
Formato:	article
Lenguaje:	EN
Publicado:	Pouyan Press 2021
Materias:	meta-heuristic algorithms suspended sediment load estimation zarinehrood river Technology T
Acceso en línea:	https://doaj.org/article/02920b25137246e8862b75e1905829ce
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spelling	oai:doaj.org-article:02920b25137246e8862b75e1905829ce2021-11-11T11:41:52ZApplication of Whale Optimization Algorithm Combined with Adaptive Neuro-Fuzzy Inference System for Estimating Suspended Sediment Load2588-287210.22115/scce.2021.281972.1300https://doaj.org/article/02920b25137246e8862b75e1905829ce2021-07-01T00:00:00Zhttp://www.jsoftcivil.com/article_133428_6eced291a0f67838f56a6b74fa85ed8a.pdfhttps://doaj.org/toc/2588-2872In Iran, no detailed information on the amount of erosion, sediment transport, and sedimentation of rivers, and in many cases, there are many differences between measurements. Since the flow regime and consequently the sediment regime in the drainage basins are not constant, estimation of sediment discharge can help estimate the sediment accumulated behind the water structures, especially the dams, and determining the dead volume of reservoirs in the coming months, and by adopting timely arrangements, the management of discharge will be facilitated to a certain extent during sedimentation. In this study, a hybrid method of the Whale optimization algorithm and the neuro-fuzzy inference system was used to estimate the suspended sediment load (SLL) of the Zarinehrood river. The performance of the proposed methods was evaluated by two statistics, including determination coefficient (R2) and normal root mean square error (NRMSE). SSL of the Zarinehrood river during 10 years with flow discharge was used as inputs. The results showed the high accuracy of the WOA-ANFIS with values R2=0.962 and NRMSE=0.051. In general, a comparison of the results obtained from the hybrid method used in this study showed the high ability and accuracy of the WOA-ANFIS method in estimating the SLL of the Zarinhrood river.Hojjat EmamiSomayeh EmamiPouyan Pressarticlemeta-heuristic algorithmssuspended sediment loadestimationzarinehrood riverTechnologyTENJournal of Soft Computing in Civil Engineering, Vol 5, Iss 3, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	meta-heuristic algorithms suspended sediment load estimation zarinehrood river Technology T
spellingShingle	meta-heuristic algorithms suspended sediment load estimation zarinehrood river Technology T Hojjat Emami Somayeh Emami Application of Whale Optimization Algorithm Combined with Adaptive Neuro-Fuzzy Inference System for Estimating Suspended Sediment Load
description	In Iran, no detailed information on the amount of erosion, sediment transport, and sedimentation of rivers, and in many cases, there are many differences between measurements. Since the flow regime and consequently the sediment regime in the drainage basins are not constant, estimation of sediment discharge can help estimate the sediment accumulated behind the water structures, especially the dams, and determining the dead volume of reservoirs in the coming months, and by adopting timely arrangements, the management of discharge will be facilitated to a certain extent during sedimentation. In this study, a hybrid method of the Whale optimization algorithm and the neuro-fuzzy inference system was used to estimate the suspended sediment load (SLL) of the Zarinehrood river. The performance of the proposed methods was evaluated by two statistics, including determination coefficient (R2) and normal root mean square error (NRMSE). SSL of the Zarinehrood river during 10 years with flow discharge was used as inputs. The results showed the high accuracy of the WOA-ANFIS with values R2=0.962 and NRMSE=0.051. In general, a comparison of the results obtained from the hybrid method used in this study showed the high ability and accuracy of the WOA-ANFIS method in estimating the SLL of the Zarinhrood river.
format	article
author	Hojjat Emami Somayeh Emami
author_facet	Hojjat Emami Somayeh Emami
author_sort	Hojjat Emami
title	Application of Whale Optimization Algorithm Combined with Adaptive Neuro-Fuzzy Inference System for Estimating Suspended Sediment Load
title_short	Application of Whale Optimization Algorithm Combined with Adaptive Neuro-Fuzzy Inference System for Estimating Suspended Sediment Load
title_full	Application of Whale Optimization Algorithm Combined with Adaptive Neuro-Fuzzy Inference System for Estimating Suspended Sediment Load
title_fullStr	Application of Whale Optimization Algorithm Combined with Adaptive Neuro-Fuzzy Inference System for Estimating Suspended Sediment Load
title_full_unstemmed	Application of Whale Optimization Algorithm Combined with Adaptive Neuro-Fuzzy Inference System for Estimating Suspended Sediment Load
title_sort	application of whale optimization algorithm combined with adaptive neuro-fuzzy inference system for estimating suspended sediment load
publisher	Pouyan Press
publishDate	2021
url	https://doaj.org/article/02920b25137246e8862b75e1905829ce
work_keys_str_mv	AT hojjatemami applicationofwhaleoptimizationalgorithmcombinedwithadaptiveneurofuzzyinferencesystemforestimatingsuspendedsedimentload AT somayehemami applicationofwhaleoptimizationalgorithmcombinedwithadaptiveneurofuzzyinferencesystemforestimatingsuspendedsedimentload
_version_	1718439097277612032

Application of Whale Optimization Algorithm Combined with Adaptive Neuro-Fuzzy Inference System for Estimating Suspended Sediment Load

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