A genetic algorithm-based support vector machine to estimate the transverse mixing coefficient in streams

Transverse mixing coefficient (TMC) is known as one of the most effective parameters in the two-dimensional simulation of water pollution, and increasing the accuracy of estimating this coefficient will improve the modeling process. In the present study, genetic algorithm (GA)-based support vector m...

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Autores principales:	Hosein Nezaratian, Javad Zahiri, Mohammad Fatehi Peykani, AmirHamzeh Haghiabi, Abbas Parsaie
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	ga-svm algorithm pollution sensitivity analysis transverse mixing coefficient Environmental technology. Sanitary engineering TD1-1066
Acceso en línea:	https://doaj.org/article/3904f90570f0412e82e1a8a05c8fa5f5
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id	oai:doaj.org-article:3904f90570f0412e82e1a8a05c8fa5f5
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spelling	oai:doaj.org-article:3904f90570f0412e82e1a8a05c8fa5f52021-11-06T03:51:15ZA genetic algorithm-based support vector machine to estimate the transverse mixing coefficient in streams2709-80442709-805210.2166/wqrj.2021.003https://doaj.org/article/3904f90570f0412e82e1a8a05c8fa5f52021-08-01T00:00:00Zhttp://wqrjc.iwaponline.com/content/56/3/127https://doaj.org/toc/2709-8044https://doaj.org/toc/2709-8052Transverse mixing coefficient (TMC) is known as one of the most effective parameters in the two-dimensional simulation of water pollution, and increasing the accuracy of estimating this coefficient will improve the modeling process. In the present study, genetic algorithm (GA)-based support vector machine (SVM) was used to estimate TMC in streams. There are three principal parameters in SVM which need to be adjusted during the estimating procedure. GA helps SVM and optimizes these three parameters automatically in the best way. The accuracy of the SVM and GA-SVM algorithms along with previous models were discussed in TMC estimation by using a wide range of hydraulic and geometrical data from field and laboratory experiments. According to statistical analysis, the performance of the mentioned models in both straight and meandering streams was more accurate than the regression-based models. Sensitivity analysis showed that the accuracy of the GA-SVM algorithm in TMC estimation significantly correlated with the number of input parameters. Eliminating the uncorrelated parameters and reducing the number of input parameters will reduce the complexity of the problem and improve the TMC estimation by GA-SVM. HIGHLIGHTS Genetic algorithm (GA)-based support vector machine (SVM) was used to estimate TMC in streams.; Sensitivity analysis showed that the accuracy of GA-SVM algorithm in TMC estimation significantly correlated with the number of input parameters.;Hosein NezaratianJavad ZahiriMohammad Fatehi PeykaniAmirHamzeh HaghiabiAbbas ParsaieIWA Publishingarticlega-svm algorithmpollutionsensitivity analysistransverse mixing coefficientEnvironmental technology. Sanitary engineeringTD1-1066ENWater Quality Research Journal, Vol 56, Iss 3, Pp 127-142 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	ga-svm algorithm pollution sensitivity analysis transverse mixing coefficient Environmental technology. Sanitary engineering TD1-1066
spellingShingle	ga-svm algorithm pollution sensitivity analysis transverse mixing coefficient Environmental technology. Sanitary engineering TD1-1066 Hosein Nezaratian Javad Zahiri Mohammad Fatehi Peykani AmirHamzeh Haghiabi Abbas Parsaie A genetic algorithm-based support vector machine to estimate the transverse mixing coefficient in streams
description	Transverse mixing coefficient (TMC) is known as one of the most effective parameters in the two-dimensional simulation of water pollution, and increasing the accuracy of estimating this coefficient will improve the modeling process. In the present study, genetic algorithm (GA)-based support vector machine (SVM) was used to estimate TMC in streams. There are three principal parameters in SVM which need to be adjusted during the estimating procedure. GA helps SVM and optimizes these three parameters automatically in the best way. The accuracy of the SVM and GA-SVM algorithms along with previous models were discussed in TMC estimation by using a wide range of hydraulic and geometrical data from field and laboratory experiments. According to statistical analysis, the performance of the mentioned models in both straight and meandering streams was more accurate than the regression-based models. Sensitivity analysis showed that the accuracy of the GA-SVM algorithm in TMC estimation significantly correlated with the number of input parameters. Eliminating the uncorrelated parameters and reducing the number of input parameters will reduce the complexity of the problem and improve the TMC estimation by GA-SVM. HIGHLIGHTS Genetic algorithm (GA)-based support vector machine (SVM) was used to estimate TMC in streams.; Sensitivity analysis showed that the accuracy of GA-SVM algorithm in TMC estimation significantly correlated with the number of input parameters.;
format	article
author	Hosein Nezaratian Javad Zahiri Mohammad Fatehi Peykani AmirHamzeh Haghiabi Abbas Parsaie
author_facet	Hosein Nezaratian Javad Zahiri Mohammad Fatehi Peykani AmirHamzeh Haghiabi Abbas Parsaie
author_sort	Hosein Nezaratian
title	A genetic algorithm-based support vector machine to estimate the transverse mixing coefficient in streams
title_short	A genetic algorithm-based support vector machine to estimate the transverse mixing coefficient in streams
title_full	A genetic algorithm-based support vector machine to estimate the transverse mixing coefficient in streams
title_fullStr	A genetic algorithm-based support vector machine to estimate the transverse mixing coefficient in streams
title_full_unstemmed	A genetic algorithm-based support vector machine to estimate the transverse mixing coefficient in streams
title_sort	genetic algorithm-based support vector machine to estimate the transverse mixing coefficient in streams
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/3904f90570f0412e82e1a8a05c8fa5f5
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A genetic algorithm-based support vector machine to estimate the transverse mixing coefficient in streams

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