Development of artificial intelligence models for well groundwater quality simulation: Different modeling scenarios.

Groundwater is one of the most important freshwater resources, especially in arid and semi-arid regions where the annual amounts of precipitation are small with frequent drought durations. Information on qualitative parameters of these valuable resources is very crucial as it might affect its applic...

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Autores principales:	Naser Shiri, Jalal Shiri, Zaher Mundher Yaseen, Sungwon Kim, Il-Moon Chung, Vahid Nourani, Mohammad Zounemat-Kermani
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/74aee35aff674914b52f0039103ac8a0
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spelling	oai:doaj.org-article:74aee35aff674914b52f0039103ac8a02021-11-25T05:54:13ZDevelopment of artificial intelligence models for well groundwater quality simulation: Different modeling scenarios.1932-620310.1371/journal.pone.0251510https://doaj.org/article/74aee35aff674914b52f0039103ac8a02021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0251510https://doaj.org/toc/1932-6203Groundwater is one of the most important freshwater resources, especially in arid and semi-arid regions where the annual amounts of precipitation are small with frequent drought durations. Information on qualitative parameters of these valuable resources is very crucial as it might affect its applicability from agricultural, drinking, and industrial aspects. Although geo-statistics methods can provide insight about spatial distribution of quality factors, applications of advanced artificial intelligence (AI) models can contribute to produce more accurate results as robust alternative for such a complex geo-science problem. The present research investigates the capacity of several types of AI models for modeling four key water quality variables namely electrical conductivity (EC), sodium adsorption ratio (SAR), total dissolved solid (TDS) and Sulfate (SO4) using dataset obtained from 90 wells in Tabriz Plain, Iran; assessed by k-fold testing. Two different modeling scenarios were established to make simulations using other quality parameters and the geographical information. The obtained results confirmed the capabilities of the AI models for modeling the well groundwater quality variables. Among all the applied AI models, the developed hybrid support vector machine-firefly algorithm (SVM-FFA) model achieved the best predictability performance for both investigated scenarios. The introduced computer aid methodology provided a reliable technology for groundwater monitoring and assessment.Naser ShiriJalal ShiriZaher Mundher YaseenSungwon KimIl-Moon ChungVahid NouraniMohammad Zounemat-KermaniPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 5, p e0251510 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Naser Shiri Jalal Shiri Zaher Mundher Yaseen Sungwon Kim Il-Moon Chung Vahid Nourani Mohammad Zounemat-Kermani Development of artificial intelligence models for well groundwater quality simulation: Different modeling scenarios.
description	Groundwater is one of the most important freshwater resources, especially in arid and semi-arid regions where the annual amounts of precipitation are small with frequent drought durations. Information on qualitative parameters of these valuable resources is very crucial as it might affect its applicability from agricultural, drinking, and industrial aspects. Although geo-statistics methods can provide insight about spatial distribution of quality factors, applications of advanced artificial intelligence (AI) models can contribute to produce more accurate results as robust alternative for such a complex geo-science problem. The present research investigates the capacity of several types of AI models for modeling four key water quality variables namely electrical conductivity (EC), sodium adsorption ratio (SAR), total dissolved solid (TDS) and Sulfate (SO4) using dataset obtained from 90 wells in Tabriz Plain, Iran; assessed by k-fold testing. Two different modeling scenarios were established to make simulations using other quality parameters and the geographical information. The obtained results confirmed the capabilities of the AI models for modeling the well groundwater quality variables. Among all the applied AI models, the developed hybrid support vector machine-firefly algorithm (SVM-FFA) model achieved the best predictability performance for both investigated scenarios. The introduced computer aid methodology provided a reliable technology for groundwater monitoring and assessment.
format	article
author	Naser Shiri Jalal Shiri Zaher Mundher Yaseen Sungwon Kim Il-Moon Chung Vahid Nourani Mohammad Zounemat-Kermani
author_facet	Naser Shiri Jalal Shiri Zaher Mundher Yaseen Sungwon Kim Il-Moon Chung Vahid Nourani Mohammad Zounemat-Kermani
author_sort	Naser Shiri
title	Development of artificial intelligence models for well groundwater quality simulation: Different modeling scenarios.
title_short	Development of artificial intelligence models for well groundwater quality simulation: Different modeling scenarios.
title_full	Development of artificial intelligence models for well groundwater quality simulation: Different modeling scenarios.
title_fullStr	Development of artificial intelligence models for well groundwater quality simulation: Different modeling scenarios.
title_full_unstemmed	Development of artificial intelligence models for well groundwater quality simulation: Different modeling scenarios.
title_sort	development of artificial intelligence models for well groundwater quality simulation: different modeling scenarios.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/74aee35aff674914b52f0039103ac8a0
work_keys_str_mv	AT nasershiri developmentofartificialintelligencemodelsforwellgroundwaterqualitysimulationdifferentmodelingscenarios AT jalalshiri developmentofartificialintelligencemodelsforwellgroundwaterqualitysimulationdifferentmodelingscenarios AT zahermundheryaseen developmentofartificialintelligencemodelsforwellgroundwaterqualitysimulationdifferentmodelingscenarios AT sungwonkim developmentofartificialintelligencemodelsforwellgroundwaterqualitysimulationdifferentmodelingscenarios AT ilmoonchung developmentofartificialintelligencemodelsforwellgroundwaterqualitysimulationdifferentmodelingscenarios AT vahidnourani developmentofartificialintelligencemodelsforwellgroundwaterqualitysimulationdifferentmodelingscenarios AT mohammadzounematkermani developmentofartificialintelligencemodelsforwellgroundwaterqualitysimulationdifferentmodelingscenarios
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Development of artificial intelligence models for well groundwater quality simulation: Different modeling scenarios.

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