Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/91509
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dc.contributorDepartment of Civil and Environmental Engineering-
dc.creatorBand, SS-
dc.creatorHeggy, E-
dc.creatorBateni, SM-
dc.creatorKarami, H-
dc.creatorRabiee, M-
dc.creatorSamadianfard, S-
dc.creatorChau, KW-
dc.creatorMosavi, A-
dc.date.accessioned2021-11-03T06:54:16Z-
dc.date.available2021-11-03T06:54:16Z-
dc.identifier.issn1994-2060-
dc.identifier.urihttp://hdl.handle.net/10397/91509-
dc.language.isoenen_US
dc.publisherHong Kong Polytechnic University, Department of Civil and Structural Engineeringen_US
dc.rights© 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en_US
dc.rightsThe following publication Band, S. S., Heggy, E., Bateni, S. M., Karami, H., Rabiee, M., Samadianfard, S., ... & Mosavi, A. (2021). Groundwater level prediction in arid areas using wavelet analysis and Gaussian process regression. Engineering Applications of Computational Fluid Mechanics, 15(1), 1147-1158 is available at https://doi.org/10.1080/19942060.2021.1944913en_US
dc.subjectArtificial intelligenceen_US
dc.subjectGaussian process regressionen_US
dc.subjectGroundwater level predictionen_US
dc.subjectHydrological modelen_US
dc.subjectMachine learningen_US
dc.subjectSupport vectoren_US
dc.titleGroundwater level prediction in arid areas using wavelet analysis and gaussian process regressionen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage1147-
dc.identifier.epage1158-
dc.identifier.volume15-
dc.identifier.issue1-
dc.identifier.doi10.1080/19942060.2021.1944913-
dcterms.abstractUtilizing new approaches to accurately predict groundwater level (GWL) in arid regions is of vital importance. In this study, support vector regression (SVR), Gaussian process regression (GPR), and their combination with wavelet transformation (named wavelet-support vector regression (W-SVR) and wavelet-Gaussian process regression (W-GPR)) are used to forecast groundwater level in Semnan plain (arid area) for the next month. Three different wavelet transformations, namely Haar, db4, and Symlet, are tested. Four statistical metrics, namely root mean square error (RMSE), mean absolute percentage error (MAPE), coefficient of determination (R 2), and Nah-Sutcliffe efficiency (NS), are used to evaluate performance of different methods. The results reveal that SVR with RMSE of 0.04790 (m), MAPE of 0.00199%, R 2 of 0.99995, and NS of 0.99988 significantly outperforms GPR with RMSE of 0.55439 (m), MAPE of 0.04363%, R2 of 0.99264, and NS of 0.98413. Besides, the hybrid W-GPR-1 model (i.e. GPR with Harr wavelet) remarkably improves the accuracy of GWL prediction compared to GPR. Finally, the hybrid W-SVR-3 model (i.e. SVR with Symlet) provides the best GWL prediction with RMSE, MAPE, R2, and NS of 0.01290 (m), 0.00079%, 0.99999, and 0.99999, respectively. Overall, the findings indicate that hybrid models can accurately predict GWL in arid regions.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationEngineering applications of computational fluid mechanics, 2021, v. 15, no. 1, p. 1147-1158-
dcterms.isPartOfEngineering applications of computational fluid mechanics-
dcterms.issued2021-
dc.identifier.scopus2-s2.0-85110346643-
dc.identifier.eissn1997-003X-
dc.description.validate202110 bcvc-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOSen_US
dc.description.pubStatusPublisheden_US
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