Combination of neuro-fuzzy network and genetic algorithm for estimating discharge capacity of triangular in plan weirs

In the current study, a new hybrid of the genetic algorithm (GA) and adaptive Neuro-fuzzy inference system (ANFIS) was introduced to model the discharge coefficient (DC) of triangular weirs. The genetic algorithm was implemented for increasing the efficiency of ANFIS by adjusting membership function...

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Autores principales:	Ali Azizpor, Ahmad Rajabi, Fariborz Yosefvand, Saeid Shabanlou
Formato:	article
Lenguaje:	EN
Publicado:	Razi University 2021
Materias:	anfis discharge coefficient genetic algorithm hybrid model triangular in plan weirs Environmental technology. Sanitary engineering TD1-1066
Acceso en línea:	https://doaj.org/article/00b147862bf941089593e080945ed483
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spelling	oai:doaj.org-article:00b147862bf941089593e080945ed4832021-11-20T12:24:01ZCombination of neuro-fuzzy network and genetic algorithm for estimating discharge capacity of triangular in plan weirs2476-628310.22126/arww.2020.5269.1169https://doaj.org/article/00b147862bf941089593e080945ed4832021-06-01T00:00:00Zhttps://arww.razi.ac.ir/article_1497_e932aac2eceb5240223c0fdb4be496b4.pdfhttps://doaj.org/toc/2476-6283In the current study, a new hybrid of the genetic algorithm (GA) and adaptive Neuro-fuzzy inference system (ANFIS) was introduced to model the discharge coefficient (DC) of triangular weirs. The genetic algorithm was implemented for increasing the efficiency of ANFIS by adjusting membership functions as well as minimizing error values. To evaluate the proficiency of the proposed hybrid method, the Monte Carlo simulations (MCS) and the k-fold validation method (k=5) was applied. The results of developed hybrid model indicate that the weir vortex angle, flow Froude number, the ratio of the weir length to its height, the ratio of the channel width to the weir length and ratio of the flow head to the weir height are the most effective parameters in the DC estimation. The quantitative examination of the proposed hybrid method indicates that the Root Mean Square Error (RMSE) and Mean Absolute Percent Error (MAPE) are as 0.016 and 1.647 (respectively) for the superior model. Besides, the Froude number is found as the most effective variable in DC modeling through the quantitative analysis. A comparison of the developed hybrid ANFIS-GA with the individual ANFIS model in the DC estimation indicates the hybrid model outperformed than the individual one.Ali AzizporAhmad RajabiFariborz YosefvandSaeid ShabanlouRazi Universityarticleanfisdischarge coefficientgenetic algorithmhybrid modeltriangular in plan weirsEnvironmental technology. Sanitary engineeringTD1-1066ENJournal of Applied Research in Water and Wastewater , Vol 8, Iss 1, Pp 1-6 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	anfis discharge coefficient genetic algorithm hybrid model triangular in plan weirs Environmental technology. Sanitary engineering TD1-1066
spellingShingle	anfis discharge coefficient genetic algorithm hybrid model triangular in plan weirs Environmental technology. Sanitary engineering TD1-1066 Ali Azizpor Ahmad Rajabi Fariborz Yosefvand Saeid Shabanlou Combination of neuro-fuzzy network and genetic algorithm for estimating discharge capacity of triangular in plan weirs
description	In the current study, a new hybrid of the genetic algorithm (GA) and adaptive Neuro-fuzzy inference system (ANFIS) was introduced to model the discharge coefficient (DC) of triangular weirs. The genetic algorithm was implemented for increasing the efficiency of ANFIS by adjusting membership functions as well as minimizing error values. To evaluate the proficiency of the proposed hybrid method, the Monte Carlo simulations (MCS) and the k-fold validation method (k=5) was applied. The results of developed hybrid model indicate that the weir vortex angle, flow Froude number, the ratio of the weir length to its height, the ratio of the channel width to the weir length and ratio of the flow head to the weir height are the most effective parameters in the DC estimation. The quantitative examination of the proposed hybrid method indicates that the Root Mean Square Error (RMSE) and Mean Absolute Percent Error (MAPE) are as 0.016 and 1.647 (respectively) for the superior model. Besides, the Froude number is found as the most effective variable in DC modeling through the quantitative analysis. A comparison of the developed hybrid ANFIS-GA with the individual ANFIS model in the DC estimation indicates the hybrid model outperformed than the individual one.
format	article
author	Ali Azizpor Ahmad Rajabi Fariborz Yosefvand Saeid Shabanlou
author_facet	Ali Azizpor Ahmad Rajabi Fariborz Yosefvand Saeid Shabanlou
author_sort	Ali Azizpor
title	Combination of neuro-fuzzy network and genetic algorithm for estimating discharge capacity of triangular in plan weirs
title_short	Combination of neuro-fuzzy network and genetic algorithm for estimating discharge capacity of triangular in plan weirs
title_full	Combination of neuro-fuzzy network and genetic algorithm for estimating discharge capacity of triangular in plan weirs
title_fullStr	Combination of neuro-fuzzy network and genetic algorithm for estimating discharge capacity of triangular in plan weirs
title_full_unstemmed	Combination of neuro-fuzzy network and genetic algorithm for estimating discharge capacity of triangular in plan weirs
title_sort	combination of neuro-fuzzy network and genetic algorithm for estimating discharge capacity of triangular in plan weirs
publisher	Razi University
publishDate	2021
url	https://doaj.org/article/00b147862bf941089593e080945ed483
work_keys_str_mv	AT aliazizpor combinationofneurofuzzynetworkandgeneticalgorithmforestimatingdischargecapacityoftriangularinplanweirs AT ahmadrajabi combinationofneurofuzzynetworkandgeneticalgorithmforestimatingdischargecapacityoftriangularinplanweirs AT fariborzyosefvand combinationofneurofuzzynetworkandgeneticalgorithmforestimatingdischargecapacityoftriangularinplanweirs AT saeidshabanlou combinationofneurofuzzynetworkandgeneticalgorithmforestimatingdischargecapacityoftriangularinplanweirs
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Combination of neuro-fuzzy network and genetic algorithm for estimating discharge capacity of triangular in plan weirs

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