Modeling of contaminant concentration using the classification-based model integrated with data preprocessing algorithms

Water quality is one of the most important factors contributing to a healthy life; meanwhile, total dissolved solids (TDS) and electrical conductivity (EC) are the most important parameters in water quality, and many water developing plans have been implemented for the recognition of these factors....

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Autores principales:	Saeed Pipelzadeh, Reza Mastouri
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	data preprocessing algorithms electrical conductivity model tree total dissolved solids water quality parameters Information technology T58.5-58.64 Environmental technology. Sanitary engineering TD1-1066
Acceso en línea:	https://doaj.org/article/3af34f7d85b745afb86f358bbeb23a33
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spelling	oai:doaj.org-article:3af34f7d85b745afb86f358bbeb23a332021-11-05T17:46:58ZModeling of contaminant concentration using the classification-based model integrated with data preprocessing algorithms1464-71411465-173410.2166/hydro.2021.138https://doaj.org/article/3af34f7d85b745afb86f358bbeb23a332021-05-01T00:00:00Zhttp://jh.iwaponline.com/content/23/3/639https://doaj.org/toc/1464-7141https://doaj.org/toc/1465-1734Water quality is one of the most important factors contributing to a healthy life; meanwhile, total dissolved solids (TDS) and electrical conductivity (EC) are the most important parameters in water quality, and many water developing plans have been implemented for the recognition of these factors. The accurate prediction of water quality parameters (WQPs) is an essential requisite for water quality management, human health, public consumption, and domestic uses. Using three novel data preprocessing algorithms (DPAs), including empirical mode decomposition (EMD), ensemble EMD (EEMD), and variational mode decomposition (VMD) to estimate two important WQPs, TDS and EC, differentiates this study from the existing literature. The acceptability and reliability of the proposed models (e.g., model tree (MT), EMD-MT, EEMD-MT, and VMD-MT) were evaluated using five performance metrics and visual plots. A comparison of the performances of standalone and hybrid models indicated that DPAs can enhance the performance of standalone MT model for both TDS and EC estimations. For instance, the VMD-MT model (root-mean-square error (RMSE) = 24.41 mg/l, ratio of RMSE to SD (RSD) = 0.231, and Nash–Sutcliffe efficiency (Ens) = 0.94 (Garmrood) and RMSE = 31.85 mg/l, RSD = 0.133, and Ens = 0.98 (Varand)) outperformed other hybrid models and original MT models for TDS estimations. Regarding the EC estimation results, as for R2, VMD could enhance the accuracy of prediction for the MT model for Garmrood and Varand stations by 10.2 and 7.6%, respectively. HIGHLIGHTS Two important water quality parameters, TDS and EC, were modeled in this study.; Three data preprocessing algorithms were used to address the nonstationarity of the dataset.; To validate proposed models, a classification-based MT was used as the benchmark model.; The VMD-MT proves to be an effective tool to provide strong technical support for WQPs.;Saeed PipelzadehReza MastouriIWA Publishingarticledata preprocessing algorithmselectrical conductivitymodel treetotal dissolved solidswater quality parametersInformation technologyT58.5-58.64Environmental technology. Sanitary engineeringTD1-1066ENJournal of Hydroinformatics, Vol 23, Iss 3, Pp 639-654 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	data preprocessing algorithms electrical conductivity model tree total dissolved solids water quality parameters Information technology T58.5-58.64 Environmental technology. Sanitary engineering TD1-1066
spellingShingle	data preprocessing algorithms electrical conductivity model tree total dissolved solids water quality parameters Information technology T58.5-58.64 Environmental technology. Sanitary engineering TD1-1066 Saeed Pipelzadeh Reza Mastouri Modeling of contaminant concentration using the classification-based model integrated with data preprocessing algorithms
description	Water quality is one of the most important factors contributing to a healthy life; meanwhile, total dissolved solids (TDS) and electrical conductivity (EC) are the most important parameters in water quality, and many water developing plans have been implemented for the recognition of these factors. The accurate prediction of water quality parameters (WQPs) is an essential requisite for water quality management, human health, public consumption, and domestic uses. Using three novel data preprocessing algorithms (DPAs), including empirical mode decomposition (EMD), ensemble EMD (EEMD), and variational mode decomposition (VMD) to estimate two important WQPs, TDS and EC, differentiates this study from the existing literature. The acceptability and reliability of the proposed models (e.g., model tree (MT), EMD-MT, EEMD-MT, and VMD-MT) were evaluated using five performance metrics and visual plots. A comparison of the performances of standalone and hybrid models indicated that DPAs can enhance the performance of standalone MT model for both TDS and EC estimations. For instance, the VMD-MT model (root-mean-square error (RMSE) = 24.41 mg/l, ratio of RMSE to SD (RSD) = 0.231, and Nash–Sutcliffe efficiency (Ens) = 0.94 (Garmrood) and RMSE = 31.85 mg/l, RSD = 0.133, and Ens = 0.98 (Varand)) outperformed other hybrid models and original MT models for TDS estimations. Regarding the EC estimation results, as for R2, VMD could enhance the accuracy of prediction for the MT model for Garmrood and Varand stations by 10.2 and 7.6%, respectively. HIGHLIGHTS Two important water quality parameters, TDS and EC, were modeled in this study.; Three data preprocessing algorithms were used to address the nonstationarity of the dataset.; To validate proposed models, a classification-based MT was used as the benchmark model.; The VMD-MT proves to be an effective tool to provide strong technical support for WQPs.;
format	article
author	Saeed Pipelzadeh Reza Mastouri
author_facet	Saeed Pipelzadeh Reza Mastouri
author_sort	Saeed Pipelzadeh
title	Modeling of contaminant concentration using the classification-based model integrated with data preprocessing algorithms
title_short	Modeling of contaminant concentration using the classification-based model integrated with data preprocessing algorithms
title_full	Modeling of contaminant concentration using the classification-based model integrated with data preprocessing algorithms
title_fullStr	Modeling of contaminant concentration using the classification-based model integrated with data preprocessing algorithms
title_full_unstemmed	Modeling of contaminant concentration using the classification-based model integrated with data preprocessing algorithms
title_sort	modeling of contaminant concentration using the classification-based model integrated with data preprocessing algorithms
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/3af34f7d85b745afb86f358bbeb23a33
work_keys_str_mv	AT saeedpipelzadeh modelingofcontaminantconcentrationusingtheclassificationbasedmodelintegratedwithdatapreprocessingalgorithms AT rezamastouri modelingofcontaminantconcentrationusingtheclassificationbasedmodelintegratedwithdatapreprocessingalgorithms
_version_	1718444096264077312

Modeling of contaminant concentration using the classification-based model integrated with data preprocessing algorithms

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