Multivariate modeling of groundwater quality using hybrid evolutionary soft-computing methods in various climatic condition areas of Iran

In the current study, several soft-computing methods including artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), gene expression programming (GEP), and hybrid wavelet theory-GEP (WGEP) are used for modeling the groundwater's electrical conductivity (EC) variable....

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Alireza Emadi, Sarvin Zamanzad-Ghavidel, Reza Sobhani, Ali Rashid-Niaghi
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	climatic condition groundwater quality modeling soft-computing wavelet theory Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/b4854af995894ea198a82c92e5f0b947
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:b4854af995894ea198a82c92e5f0b947
record_format	dspace
spelling	oai:doaj.org-article:b4854af995894ea198a82c92e5f0b9472021-11-05T17:03:16ZMultivariate modeling of groundwater quality using hybrid evolutionary soft-computing methods in various climatic condition areas of Iran2709-80282709-803610.2166/aqua.2021.150https://doaj.org/article/b4854af995894ea198a82c92e5f0b9472021-05-01T00:00:00Zhttp://aqua.iwaponline.com/content/70/3/328https://doaj.org/toc/2709-8028https://doaj.org/toc/2709-8036In the current study, several soft-computing methods including artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), gene expression programming (GEP), and hybrid wavelet theory-GEP (WGEP) are used for modeling the groundwater's electrical conductivity (EC) variable. Hence, the groundwater samples from three sources (deep well, semi-deep well, and aqueducts), located in six basins of Iran (Urmia Lake (UL), Sefid-rud (SR), Karkheh (K), Kavir-Markazi (KM), Gavkhouni (G), and Hamun-e Jaz Murian (HJM)) with various climate conditions, were collected during 2004–2018. The results of the WGEP model with data de-noising showed the best performance in estimating the EC variable, considering all types of groundwater resources with various climatic conditions. The Root Mean Squared Error (RMSE) values of the WGEP model were varied from 162.068 to 348.911, 73.802 to 171.376, 29.465 to 351.489, 118.149 to 311.798, 217.667 to 430.730, and 76.253 to 162.992 μScm−1 in the areas of UL, SR, K, KM, G, and HJM basins. The WGEP model's performance (R-values) for deep wells, semi-deep wells, and aqueducts of the areas of the KM basin associated with the arid steppe cold (Bsk) dominant climate classification was the best. Also, the WGEP's extracted mathematical equations could be used for EC estimating in other basins. HIGHLIGHTS Iran's groundwater resources face a critical situation.; The Electrical Conductivity (EC) variable of various groundwater resources was estimated using single and hybrid wavelet theory methods.; The impact of various climatic categories on the EC estimation was evaluated.; The data de-noising by wavelet tools can improve the performance of EC estimation models.;Alireza EmadiSarvin Zamanzad-GhavidelReza SobhaniAli Rashid-NiaghiIWA Publishingarticleclimatic conditiongroundwater qualitymodelingsoft-computingwavelet theoryEnvironmental technology. Sanitary engineeringTD1-1066Environmental sciencesGE1-350ENAqua, Vol 70, Iss 3, Pp 328-341 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	climatic condition groundwater quality modeling soft-computing wavelet theory Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350
spellingShingle	climatic condition groundwater quality modeling soft-computing wavelet theory Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 Alireza Emadi Sarvin Zamanzad-Ghavidel Reza Sobhani Ali Rashid-Niaghi Multivariate modeling of groundwater quality using hybrid evolutionary soft-computing methods in various climatic condition areas of Iran
description	In the current study, several soft-computing methods including artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), gene expression programming (GEP), and hybrid wavelet theory-GEP (WGEP) are used for modeling the groundwater's electrical conductivity (EC) variable. Hence, the groundwater samples from three sources (deep well, semi-deep well, and aqueducts), located in six basins of Iran (Urmia Lake (UL), Sefid-rud (SR), Karkheh (K), Kavir-Markazi (KM), Gavkhouni (G), and Hamun-e Jaz Murian (HJM)) with various climate conditions, were collected during 2004–2018. The results of the WGEP model with data de-noising showed the best performance in estimating the EC variable, considering all types of groundwater resources with various climatic conditions. The Root Mean Squared Error (RMSE) values of the WGEP model were varied from 162.068 to 348.911, 73.802 to 171.376, 29.465 to 351.489, 118.149 to 311.798, 217.667 to 430.730, and 76.253 to 162.992 μScm−1 in the areas of UL, SR, K, KM, G, and HJM basins. The WGEP model's performance (R-values) for deep wells, semi-deep wells, and aqueducts of the areas of the KM basin associated with the arid steppe cold (Bsk) dominant climate classification was the best. Also, the WGEP's extracted mathematical equations could be used for EC estimating in other basins. HIGHLIGHTS Iran's groundwater resources face a critical situation.; The Electrical Conductivity (EC) variable of various groundwater resources was estimated using single and hybrid wavelet theory methods.; The impact of various climatic categories on the EC estimation was evaluated.; The data de-noising by wavelet tools can improve the performance of EC estimation models.;
format	article
author	Alireza Emadi Sarvin Zamanzad-Ghavidel Reza Sobhani Ali Rashid-Niaghi
author_facet	Alireza Emadi Sarvin Zamanzad-Ghavidel Reza Sobhani Ali Rashid-Niaghi
author_sort	Alireza Emadi
title	Multivariate modeling of groundwater quality using hybrid evolutionary soft-computing methods in various climatic condition areas of Iran
title_short	Multivariate modeling of groundwater quality using hybrid evolutionary soft-computing methods in various climatic condition areas of Iran
title_full	Multivariate modeling of groundwater quality using hybrid evolutionary soft-computing methods in various climatic condition areas of Iran
title_fullStr	Multivariate modeling of groundwater quality using hybrid evolutionary soft-computing methods in various climatic condition areas of Iran
title_full_unstemmed	Multivariate modeling of groundwater quality using hybrid evolutionary soft-computing methods in various climatic condition areas of Iran
title_sort	multivariate modeling of groundwater quality using hybrid evolutionary soft-computing methods in various climatic condition areas of iran
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/b4854af995894ea198a82c92e5f0b947
work_keys_str_mv	AT alirezaemadi multivariatemodelingofgroundwaterqualityusinghybridevolutionarysoftcomputingmethodsinvariousclimaticconditionareasofiran AT sarvinzamanzadghavidel multivariatemodelingofgroundwaterqualityusinghybridevolutionarysoftcomputingmethodsinvariousclimaticconditionareasofiran AT rezasobhani multivariatemodelingofgroundwaterqualityusinghybridevolutionarysoftcomputingmethodsinvariousclimaticconditionareasofiran AT alirashidniaghi multivariatemodelingofgroundwaterqualityusinghybridevolutionarysoftcomputingmethodsinvariousclimaticconditionareasofiran
_version_	1718444120921341952

Multivariate modeling of groundwater quality using hybrid evolutionary soft-computing methods in various climatic condition areas of Iran

Ejemplares similares