Estimation of Water Quality Parameters in the Sepidrood River by ANFIS, GEP and LS-SVM Models
Rivers are the most important water supply resource for the drinkable, agricultural and industrial demands. Therefore, estimation of water quality parameters in rivers is an essential and necessary task. This research applies the Adaptive Neuro-Fuzzy Inference System (ANFIS), the Least Squares-Suppo...
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Water and Wastewater Consulting Engineers Research Development
2020-11-01
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doaj-3a97554d7e1c49018ef087bdee81beb62021-04-02T18:51:55ZengWater and Wastewater Consulting Engineers Research Developmentآب و فاضلاب1024-59362383-09052020-11-0131511010.22093/wwj.2020.187271.2873112798Estimation of Water Quality Parameters in the Sepidrood River by ANFIS, GEP and LS-SVM ModelsArash Adib0Hiwa Farajpanah1Mohammad Mahmoudian Shoushtari2Iman Ahmadeanfar3Prof., Civil Engineering Dept., Engineering Faculty, Shahid Chamran University of Ahvaz, Ahvaz, IranMSc Student, Civil Engineering Dept., Engineering Faculty, Shahid Chamran University of Ahvaz, Ahvaz, IranProf., Civil Engineering Dept., Engineering Faculty, Shahid Chamran University of Ahvaz, Ahvaz, IranAssist. Prof., Civil Engineering Dept. Engineering Faculty, Behbahan Khatam Al-Anbia University of Technology, Behbahan, IranRivers are the most important water supply resource for the drinkable, agricultural and industrial demands. Therefore, estimation of water quality parameters in rivers is an essential and necessary task. This research applies the Adaptive Neuro-Fuzzy Inference System (ANFIS), the Least Squares-Support Vector Machines (LS-SVM) and the Gene Expression Programming (GEP) for estimation of Total Dissolved Solids (TDS), Electrical Conductivity (EC) and Total Hardness (TH) in the Sepidrood River and a 40 year period. The applied performance criteria are the correlation coefficient (R), the Nash-Sutcliffe model Efficiency coefficient (NSE), the Normalized Mean Squared Error (NMSE) and the Mean Absolute Error (MAE). These methods have high ability for estimation of water quality parameters. The best method is LS-SVM method for estimation of TDS (RTrain=0.95 RTest=0.96). The best method is GEP method for estimation of EC (RTrain=0.94 RTest=0.95). The best method is ANFIS method for estimation of TH (RTrain=0.92 RTest=0.94). This research shows that intelligence methods can estimate unmeasured concentration of qualitative parameters by concentration of other qualitative parameters.http://www.wwjournal.ir/article_112798_c9cfc200d9ff0779f6a29e9fbc5d68ce.pdfthe sepidrood riverwater qualityanfisls-svmgep |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Arash Adib Hiwa Farajpanah Mohammad Mahmoudian Shoushtari Iman Ahmadeanfar |
spellingShingle |
Arash Adib Hiwa Farajpanah Mohammad Mahmoudian Shoushtari Iman Ahmadeanfar Estimation of Water Quality Parameters in the Sepidrood River by ANFIS, GEP and LS-SVM Models آب و فاضلاب the sepidrood river water quality anfis ls-svm gep |
author_facet |
Arash Adib Hiwa Farajpanah Mohammad Mahmoudian Shoushtari Iman Ahmadeanfar |
author_sort |
Arash Adib |
title |
Estimation of Water Quality Parameters in the Sepidrood River by ANFIS, GEP and LS-SVM Models |
title_short |
Estimation of Water Quality Parameters in the Sepidrood River by ANFIS, GEP and LS-SVM Models |
title_full |
Estimation of Water Quality Parameters in the Sepidrood River by ANFIS, GEP and LS-SVM Models |
title_fullStr |
Estimation of Water Quality Parameters in the Sepidrood River by ANFIS, GEP and LS-SVM Models |
title_full_unstemmed |
Estimation of Water Quality Parameters in the Sepidrood River by ANFIS, GEP and LS-SVM Models |
title_sort |
estimation of water quality parameters in the sepidrood river by anfis, gep and ls-svm models |
publisher |
Water and Wastewater Consulting Engineers Research Development |
series |
آب و فاضلاب |
issn |
1024-5936 2383-0905 |
publishDate |
2020-11-01 |
description |
Rivers are the most important water supply resource for the drinkable, agricultural and industrial demands. Therefore, estimation of water quality parameters in rivers is an essential and necessary task. This research applies the Adaptive Neuro-Fuzzy Inference System (ANFIS), the Least Squares-Support Vector Machines (LS-SVM) and the Gene Expression Programming (GEP) for estimation of Total Dissolved Solids (TDS), Electrical Conductivity (EC) and Total Hardness (TH) in the Sepidrood River and a 40 year period. The applied performance criteria are the correlation coefficient (R), the Nash-Sutcliffe model Efficiency coefficient (NSE), the Normalized Mean Squared Error (NMSE) and the Mean Absolute Error (MAE). These methods have high ability for estimation of water quality parameters. The best method is LS-SVM method for estimation of TDS (RTrain=0.95 RTest=0.96). The best method is GEP method for estimation of EC (RTrain=0.94 RTest=0.95). The best method is ANFIS method for estimation of TH (RTrain=0.92 RTest=0.94). This research shows that intelligence methods can estimate unmeasured concentration of qualitative parameters by concentration of other qualitative parameters. |
topic |
the sepidrood river water quality anfis ls-svm gep |
url |
http://www.wwjournal.ir/article_112798_c9cfc200d9ff0779f6a29e9fbc5d68ce.pdf |
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