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Protocol for the estimation of drinking water quality index (DWQI) in water resources: Artificial neural network (ANFIS) and Arc-Gis
Drinking water sources may be polluted by various pollutants depending on geological conditions and agricultural, industrial, and other human activities. Ensuring the safety of drinking water is, therefore, of a great importance. The purpose of this study was to assess the quality of drinking ground...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6517571/ https://www.ncbi.nlm.nih.gov/pubmed/31193115 http://dx.doi.org/10.1016/j.mex.2019.04.027 |
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author | RadFard, Majid Seif, Mozhgan Ghazizadeh Hashemi, Amir Hossein Zarei, Ahmad Saghi, Mohammad Hossein Shalyari, Naseh Morovati, Roya Heidarinejad, Zoha Samaei, Mohammad Reza |
author_facet | RadFard, Majid Seif, Mozhgan Ghazizadeh Hashemi, Amir Hossein Zarei, Ahmad Saghi, Mohammad Hossein Shalyari, Naseh Morovati, Roya Heidarinejad, Zoha Samaei, Mohammad Reza |
author_sort | RadFard, Majid |
collection | PubMed |
description | Drinking water sources may be polluted by various pollutants depending on geological conditions and agricultural, industrial, and other human activities. Ensuring the safety of drinking water is, therefore, of a great importance. The purpose of this study was to assess the quality of drinking groundwater in Bardaskan villages and to determine the water quality index. Water samples were taken from 30 villages and eighteen parameters including calcium hardness (CaH), total hardness (TH), turbidity, pH, temperature, total dissolved solids (TDS), electrical conductivity (EC), alkalinity (ALK), magnesium (Mg(2+)), calcium (Ca(2+)), potassium (K(+)), sodium (Na(+)), sulphate (SO(4)(2−)), bicarbonate (HCO(3)(−)), fluoride (F(−)), nitrate (NO(3)(−)), nitrite (NO(2)(−)) and chloride (Cl(−)) were analyzed for the purpose for this study. The water quality index of groundwater has been estimated by using the ANFIS. The spatial locations are shown using GPS. The results of this study showed that water hardness, electrical conductivity, sodium and sulfate in 66, 13, 45 and 12.5% of the studied villages were higher than the Iranian drinking water standards, respectively. Based on the Drinking Water Quality Index (DWQI), water quality in 3.3, 60, 23.3 and 13.3% of villages was excellent, good, poor and very poor, respectively. • Groundwater is one of the sources of drinking water in arid and semi-arid regions such as Bardaskan villages, which monitor the quality of these resources in planning for improving the quality of water resources. • The DWQI can clearly provide information associated with the status of water quality resources in Bardaskan villages. • The results of this study clearly indicated that with appropriate selection of input variables, ANFIS as a soft computing approach can estimate water quality indices properly and reliably. • Some parameters were in the undesirable level is some villages. Therefore, the government should try to improve the chemical and physical quality of drinking water in these areas with the necessary strategies. |
format | Online Article Text |
id | pubmed-6517571 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-65175712019-05-21 Protocol for the estimation of drinking water quality index (DWQI) in water resources: Artificial neural network (ANFIS) and Arc-Gis RadFard, Majid Seif, Mozhgan Ghazizadeh Hashemi, Amir Hossein Zarei, Ahmad Saghi, Mohammad Hossein Shalyari, Naseh Morovati, Roya Heidarinejad, Zoha Samaei, Mohammad Reza MethodsX Environmental Science Drinking water sources may be polluted by various pollutants depending on geological conditions and agricultural, industrial, and other human activities. Ensuring the safety of drinking water is, therefore, of a great importance. The purpose of this study was to assess the quality of drinking groundwater in Bardaskan villages and to determine the water quality index. Water samples were taken from 30 villages and eighteen parameters including calcium hardness (CaH), total hardness (TH), turbidity, pH, temperature, total dissolved solids (TDS), electrical conductivity (EC), alkalinity (ALK), magnesium (Mg(2+)), calcium (Ca(2+)), potassium (K(+)), sodium (Na(+)), sulphate (SO(4)(2−)), bicarbonate (HCO(3)(−)), fluoride (F(−)), nitrate (NO(3)(−)), nitrite (NO(2)(−)) and chloride (Cl(−)) were analyzed for the purpose for this study. The water quality index of groundwater has been estimated by using the ANFIS. The spatial locations are shown using GPS. The results of this study showed that water hardness, electrical conductivity, sodium and sulfate in 66, 13, 45 and 12.5% of the studied villages were higher than the Iranian drinking water standards, respectively. Based on the Drinking Water Quality Index (DWQI), water quality in 3.3, 60, 23.3 and 13.3% of villages was excellent, good, poor and very poor, respectively. • Groundwater is one of the sources of drinking water in arid and semi-arid regions such as Bardaskan villages, which monitor the quality of these resources in planning for improving the quality of water resources. • The DWQI can clearly provide information associated with the status of water quality resources in Bardaskan villages. • The results of this study clearly indicated that with appropriate selection of input variables, ANFIS as a soft computing approach can estimate water quality indices properly and reliably. • Some parameters were in the undesirable level is some villages. Therefore, the government should try to improve the chemical and physical quality of drinking water in these areas with the necessary strategies. Elsevier 2019-04-29 /pmc/articles/PMC6517571/ /pubmed/31193115 http://dx.doi.org/10.1016/j.mex.2019.04.027 Text en © 2019 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Environmental Science RadFard, Majid Seif, Mozhgan Ghazizadeh Hashemi, Amir Hossein Zarei, Ahmad Saghi, Mohammad Hossein Shalyari, Naseh Morovati, Roya Heidarinejad, Zoha Samaei, Mohammad Reza Protocol for the estimation of drinking water quality index (DWQI) in water resources: Artificial neural network (ANFIS) and Arc-Gis |
title | Protocol for the estimation of drinking water quality index (DWQI) in water resources: Artificial neural network (ANFIS) and Arc-Gis |
title_full | Protocol for the estimation of drinking water quality index (DWQI) in water resources: Artificial neural network (ANFIS) and Arc-Gis |
title_fullStr | Protocol for the estimation of drinking water quality index (DWQI) in water resources: Artificial neural network (ANFIS) and Arc-Gis |
title_full_unstemmed | Protocol for the estimation of drinking water quality index (DWQI) in water resources: Artificial neural network (ANFIS) and Arc-Gis |
title_short | Protocol for the estimation of drinking water quality index (DWQI) in water resources: Artificial neural network (ANFIS) and Arc-Gis |
title_sort | protocol for the estimation of drinking water quality index (dwqi) in water resources: artificial neural network (anfis) and arc-gis |
topic | Environmental Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6517571/ https://www.ncbi.nlm.nih.gov/pubmed/31193115 http://dx.doi.org/10.1016/j.mex.2019.04.027 |
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