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Modeling the fluctuations of groundwater level by employing ensemble deep learning techniques

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dc.citedby33
dc.contributor.authorAfan H.A.en_US
dc.contributor.authorIbrahem Ahmed Osman A.en_US
dc.contributor.authorEssam Y.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorHuang Y.F.en_US
dc.contributor.authorKisi O.en_US
dc.contributor.authorSherif M.en_US
dc.contributor.authorSefelnasr A.en_US
dc.contributor.authorChau K.-W.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid56436626600en_US
dc.contributor.authorid57221644207en_US
dc.contributor.authorid57203146903en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid55807263900en_US
dc.contributor.authorid6507051085en_US
dc.contributor.authorid7005414714en_US
dc.contributor.authorid6505592467en_US
dc.contributor.authorid7202674661en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T09:11:12Z
dc.date.available2023-05-29T09:11:12Z
dc.date.issued2021
dc.description.abstractThis study proposes two techniques: Deep Learning (DL) and Ensemble Deep Learning (EDL) to predict groundwater level (GWL) for five wells in Malaysia. Two scenarios were proposed, scenario-1 (S1): GWL from 4 wells was used as inputs to predict the GWL in the fifth well and scenario-2 (S2): time series with lag time up to 20 days for all five wells. The results from S1 prove that the ensemble EDL generally performs superior to the DL in the estimation of GWL of each station using data of remaining four wells except the Paya Indah Wetland in which the DL method provide better estimates compared to EDL. Regarding S2, the EDL also exhibits superior performance in predicting daily GWL in all five stations compared to the DL model. Implementing EDL decreased the RMSE, NAE and RRMSE by 11.6%, 27.3% and 22.3% and increased the R, Spearman rho and Kendall tau by 0.4%, 1.1% and 3.5%, respectively. Moreover, EDL for S2 shows a high level of precision within less time lag, ranging between 2 and 4 compared to DL. Therefore, the EDL model has the potential in managing the sustainability of groundwater in Malaysia. � 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1080/19942060.2021.1974093
dc.identifier.epage1439
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85115650756
dc.identifier.spage1420
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85115650756&doi=10.1080%2f19942060.2021.1974093&partnerID=40&md5=3437030c7c5bec0d0fef98ae68acf655
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26495
dc.identifier.volume15
dc.publisherTaylor and Francis Ltd.en_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleEngineering Applications of Computational Fluid Mechanics
dc.titleModeling the fluctuations of groundwater level by employing ensemble deep learning techniquesen_US
dc.typeArticleen_US
dspace.entity.typePublication
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