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Application of Artificial Intelligence Models for modeling Water Quality in Groundwater: Comprehensive Review, Evaluation and Future Trends

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dc.citedby8
dc.contributor.authorHanoon M.S.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorFai C.M.en_US
dc.contributor.authorBirima A.H.en_US
dc.contributor.authorRazzaq A.en_US
dc.contributor.authorSherif M.en_US
dc.contributor.authorSefelnasr A.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57266877500en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid57214146115en_US
dc.contributor.authorid23466519000en_US
dc.contributor.authorid57219410567en_US
dc.contributor.authorid7005414714en_US
dc.contributor.authorid6505592467en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T09:05:48Z
dc.date.available2023-05-29T09:05:48Z
dc.date.issued2021
dc.descriptionAquifers; Forecasting; Groundwater resources; Hydrogeology; Machine learning; ANN; Artificial intelligence; Artificial intelligence methods; Future trends; Groundwater quality; Hybrid model; Intelligence models; Machine learning; Quality modeling; Water quality; boron; chloride; fluoride; ground water; nitrate; phosphate; sulfate; zinc; accuracy assessment; aquifer; artificial intelligence; future prospect; groundwater; guideline; machine learning; prediction; reliability analysis; trend analysis; water quality; accuracy; alkalinity; artificial intelligence; artificial neural network; chemical oxygen demand; concentration (parameter); electric conductivity; Escherichia coli; feed forward neural network; fuzzy logic; human; machine learning; multilayer perceptron; nonhuman; pH; physical parameters; practice guideline; prediction; radial basis function; radial basis function neural network; Review; single layer perceptron; support vector machine; suspended particulate matter; total dissolved solid; total hardness; trend study; turbidity; water qualityen_US
dc.description.abstractThis study reported the state of the art of different artificial intelligence (AI) methods for groundwater quality (GWQ) modeling and introduce a brief description of common AI approaches. In addtion a bibliographic review of practices over the past two decades, was presented and attained result were compared. More than 80 journal articles from 2001 to 2021 were review in terms of characteristics and capabilities of developing methods, considering data of input-output, etc. From the reviewed studies, it could be concluded that in spite of various weaknesses, if the artificial intelligence approaches were appropriately built, they can effectively be utilized for predicting the GWQ in various aquifers. Because many steps of applying AI methods are based on trial-and-error or experience procedures, it�s helpful to review them regarding the special application for GWQ modeling. Several partial and general findings were attained from the reviewed studies that could deliver relevant guidelines for scholars who intend to carry out related work. Many new ideas in the associated area of research are also introduced in this work to develop innovative approaches and to improve the quality of prediction water quality in groundwater for example, it has been found that the combined AI models with metaheuristic optimization are more reliable in capturing the nonlinearity of water quality parameters. However, in this review few papers were found that used these hybrid models in GWQ modeling. Therefore, for future works, it is recommended to use hybrid models to more furthere investigation and enhance the reliability and accuracy of predicting in GWQ. � 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo411
dc.identifier.doi10.1007/s11270-021-05311-z
dc.identifier.issue10
dc.identifier.scopus2-s2.0-85116900440
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85116900440&doi=10.1007%2fs11270-021-05311-z&partnerID=40&md5=ac22b70033f4951431e8e719b56c7f49
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25964
dc.identifier.volume232
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.sourceScopus
dc.sourcetitleWater, Air, and Soil Pollution
dc.titleApplication of Artificial Intelligence Models for modeling Water Quality in Groundwater: Comprehensive Review, Evaluation and Future Trendsen_US
dc.typeReviewen_US
dspace.entity.typePublication
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