Experimental study and modeling of hydraulic jump for a suddenly expanding stilling basin using different hybrid algorithms

Hydraulic jump is a highly important phenomenon for dissipation of energy. This event, which involves flow regime change, can occur in many different types of stilling basins. In this study, hydraulic jump characteristics such as relative jump length and sequent depth ratio occurring in a suddenly e...

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Autores principales:	Enes Gul, O. Faruk Dursun, Abdolmajid Mohammadian
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	cross-validation evolutionary algorithm extreme learning machine hydraulic jump machine learning optimization Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506
Acceso en línea:	https://doaj.org/article/0a271ed6837b4973b864ad56d44778d6
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spelling	oai:doaj.org-article:0a271ed6837b4973b864ad56d44778d62021-11-23T18:56:45ZExperimental study and modeling of hydraulic jump for a suddenly expanding stilling basin using different hybrid algorithms1606-97491607-079810.2166/ws.2021.139https://doaj.org/article/0a271ed6837b4973b864ad56d44778d62021-11-01T00:00:00Zhttp://ws.iwaponline.com/content/21/7/3752https://doaj.org/toc/1606-9749https://doaj.org/toc/1607-0798Hydraulic jump is a highly important phenomenon for dissipation of energy. This event, which involves flow regime change, can occur in many different types of stilling basins. In this study, hydraulic jump characteristics such as relative jump length and sequent depth ratio occurring in a suddenly expanding stilling basin were estimated using hybrid extreme learning machine (ELM). To hybridize ELM, imperialist competitive algorithm (ICA), firefly algorithm (FA) and particle swarm optimization (PSO) metaheuristic algorithms were implemented. In addition, six different models were established to determine effective dimensionless (relative) input variables. A new data set was constructed by adding the data obtained from the experimental study in the present research to the data obtained from the literature. The performance of each model was evaluated using k-fold cross-validation. Results showed that ICA hybridization slightly outperformed FA and PSO methods. Considering relative input parameters, Froude number (Fr), expansion ratio (B) and relative sill height (S), effective input combinations were Fr–B–S and Fr–B for the prediction of the sequent depth ratio (Y) and relative hydraulic jump length (Lj/h1), respectively. HIGHLIGHTS Suddenly expanding stilling basins were examined both experimentally and using AI.; Hydraulic jump characteristics were estimated using hybrid extreme learning machine.; New laboratory data was modeled using novel machine learning algorithms.; Among optimization algorithms, ICA was superior to PSO and FA.; The performance of each model was evaluated using k-fold cross-validation.;Enes GulO. Faruk DursunAbdolmajid MohammadianIWA Publishingarticlecross-validationevolutionary algorithmextreme learning machinehydraulic jumpmachine learningoptimizationWater supply for domestic and industrial purposesTD201-500River, lake, and water-supply engineering (General)TC401-506ENWater Supply, Vol 21, Iss 7, Pp 3752-3771 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	cross-validation evolutionary algorithm extreme learning machine hydraulic jump machine learning optimization Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506
spellingShingle	cross-validation evolutionary algorithm extreme learning machine hydraulic jump machine learning optimization Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506 Enes Gul O. Faruk Dursun Abdolmajid Mohammadian Experimental study and modeling of hydraulic jump for a suddenly expanding stilling basin using different hybrid algorithms
description	Hydraulic jump is a highly important phenomenon for dissipation of energy. This event, which involves flow regime change, can occur in many different types of stilling basins. In this study, hydraulic jump characteristics such as relative jump length and sequent depth ratio occurring in a suddenly expanding stilling basin were estimated using hybrid extreme learning machine (ELM). To hybridize ELM, imperialist competitive algorithm (ICA), firefly algorithm (FA) and particle swarm optimization (PSO) metaheuristic algorithms were implemented. In addition, six different models were established to determine effective dimensionless (relative) input variables. A new data set was constructed by adding the data obtained from the experimental study in the present research to the data obtained from the literature. The performance of each model was evaluated using k-fold cross-validation. Results showed that ICA hybridization slightly outperformed FA and PSO methods. Considering relative input parameters, Froude number (Fr), expansion ratio (B) and relative sill height (S), effective input combinations were Fr–B–S and Fr–B for the prediction of the sequent depth ratio (Y) and relative hydraulic jump length (Lj/h1), respectively. HIGHLIGHTS Suddenly expanding stilling basins were examined both experimentally and using AI.; Hydraulic jump characteristics were estimated using hybrid extreme learning machine.; New laboratory data was modeled using novel machine learning algorithms.; Among optimization algorithms, ICA was superior to PSO and FA.; The performance of each model was evaluated using k-fold cross-validation.;
format	article
author	Enes Gul O. Faruk Dursun Abdolmajid Mohammadian
author_facet	Enes Gul O. Faruk Dursun Abdolmajid Mohammadian
author_sort	Enes Gul
title	Experimental study and modeling of hydraulic jump for a suddenly expanding stilling basin using different hybrid algorithms
title_short	Experimental study and modeling of hydraulic jump for a suddenly expanding stilling basin using different hybrid algorithms
title_full	Experimental study and modeling of hydraulic jump for a suddenly expanding stilling basin using different hybrid algorithms
title_fullStr	Experimental study and modeling of hydraulic jump for a suddenly expanding stilling basin using different hybrid algorithms
title_full_unstemmed	Experimental study and modeling of hydraulic jump for a suddenly expanding stilling basin using different hybrid algorithms
title_sort	experimental study and modeling of hydraulic jump for a suddenly expanding stilling basin using different hybrid algorithms
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/0a271ed6837b4973b864ad56d44778d6
work_keys_str_mv	AT enesgul experimentalstudyandmodelingofhydraulicjumpforasuddenlyexpandingstillingbasinusingdifferenthybridalgorithms AT ofarukdursun experimentalstudyandmodelingofhydraulicjumpforasuddenlyexpandingstillingbasinusingdifferenthybridalgorithms AT abdolmajidmohammadian experimentalstudyandmodelingofhydraulicjumpforasuddenlyexpandingstillingbasinusingdifferenthybridalgorithms
_version_	1718416135062290432

Experimental study and modeling of hydraulic jump for a suddenly expanding stilling basin using different hybrid algorithms

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