Publication:
Past, Present and Perspective Methodology for Groundwater Modeling-Based Machine Learning Approaches

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dc.citedby6
dc.contributor.authorOsman A.I.A.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorHuang Y.F.en_US
dc.contributor.authorKumar P.en_US
dc.contributor.authorBirima A.H.en_US
dc.contributor.authorSherif M.en_US
dc.contributor.authorSefelnasr A.en_US
dc.contributor.authorEbraheemand A.A.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57437554300en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid55807263900en_US
dc.contributor.authorid57206939156en_US
dc.contributor.authorid23466519000en_US
dc.contributor.authorid7005414714en_US
dc.contributor.authorid6505592467en_US
dc.contributor.authorid57437700400en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T09:36:27Z
dc.date.available2023-05-29T09:36:27Z
dc.date.issued2022
dc.descriptionMachine learning; Numerical methods; Water levels; Ground water level; Groundwater modelling; Hydrological variables; Level model; Machine learning approaches; Meteorological variables; Model method; Model-based OPC; Rapid urbanizations; Water level variations; Groundwateren_US
dc.description.abstractGrowing population and rapid urbanization are among the major causes of ground water level (GWL) depletion. Modeling GWL is considered as tough task as the GWL variation depends on various complex hydrological and meteorological variables. However, few methodologies have been proposed in literature for modeling GWL. The present research offers a summary of the most common methodologies in GWL forecasting using artificial intelligence (AI), as well as bibliographic assessments of the authors' knowledge and an overview and comparison of the findings. The characteristics and capabilities of modeling methods and the consideration of input data types and time steps have been reviewed in 40 studies published from 2010 to 2020. The reviewed studies succeeded in modeling and predicting the GWL in various regions using the methods proposed by the authors. Trial and error method in certain phases of AI modeling was helpful for testing in special applications for GWL modeling. The reviewed papers provided several partial and overall findings that may provide relevant recommendations to investigators who would like to conduct similar work in GWL modeling. In this report, a variety of new concepts for designing novel approaches and enhancing modeling efficiency are also discussed in the relevant field of analysis. Analyzing modeling methods used in all the reviewed studies it was estimated that the machine learning methods are efficient enough for modeling GWL. � 2022, The Author(s) under exclusive licence to International Center for Numerical Methods in Engineering (CIMNE).en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/s11831-022-09715-w
dc.identifier.epage3859
dc.identifier.issue6
dc.identifier.scopus2-s2.0-85123946020
dc.identifier.spage3843
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85123946020&doi=10.1007%2fs11831-022-09715-w&partnerID=40&md5=2b5ff576c9d7858fd99ba1336d3cae41
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26742
dc.identifier.volume29
dc.publisherSpringer Science and Business Media B.V.en_US
dc.sourceScopus
dc.sourcetitleArchives of Computational Methods in Engineering
dc.titlePast, Present and Perspective Methodology for Groundwater Modeling-Based Machine Learning Approachesen_US
dc.typeReviewen_US
dspace.entity.typePublication
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