Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/82214
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dc.contributorDepartment of Civil and Environmental Engineering-
dc.creatorShamshirband, S-
dc.creatorHashemi, S-
dc.creatorSalimi, H-
dc.creatorSamadianfard, S-
dc.creatorAsadi, E-
dc.creatorShadkani, S-
dc.creatorKargar, K-
dc.creatorMosavi, A-
dc.creatorNabipour, N-
dc.creatorChau, KW-
dc.date.accessioned2020-05-05T05:59:08Z-
dc.date.available2020-05-05T05:59:08Z-
dc.identifier.issn1994-2060-
dc.identifier.urihttp://hdl.handle.net/10397/82214-
dc.language.isoenen_US
dc.publisherTaylor & Francisen_US
dc.rights© 2020 The Author(s).en_US
dc.rightsThis 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 citeden_US
dc.rightsThe following publication Shahabbodin Shamshirband, Sajjad Hashemi, Hana Salimi, SaeedSamadianfard, Esmaeil Asadi, Sadra Shadkani, Katayoun Kargar, Amir Mosavi, Narjes Nabipour& Kwok-Wing Chau (2020) Predicting Standardized Streamflow index for hydrological droughtusing machine learning models, Engineering Applications of Computational Fluid Mechanics, 14:1,339-350 is available at https://dx.doi.org/10.1080/19942060.2020.1715844en_US
dc.subjectGene expression programmingen_US
dc.subjectHydrological droughten_US
dc.subjectM5 model treeen_US
dc.subjectMachine learning modelsen_US
dc.subjectStandardized streamflow indexen_US
dc.subjectSupport vector regressionen_US
dc.titlePredicting Standardized Streamflow index for hydrological drought using machine learning modelsen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage339-
dc.identifier.epage350-
dc.identifier.volume14-
dc.identifier.issue1-
dc.identifier.doi10.1080/19942060.2020.1715844-
dcterms.abstractHydrological droughts are characterized based on their duration, severity, and magnitude. Among the most critical factors, precipitation, evapotranspiration, and runoff are essential in modeling the droughts. In this study, three indices of drought, i.e., Standardized Precipitation Index (SPI), Standardized Streamflow Index (SSI), and Standardized Precipitation Evapotranspiration Index (SPEI), are modeled using Support Vector Regression (SVR), Gene Expression Programming (GEP), and M5 model trees (MT). The results indicate that SPI delivered higher accuracy. Moreover, MT model performed better in predicting SSI by a CC of 0.8195 and a RMSE of 0.8186.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationEngineering applications of computational fluid mechanics, 2020, v. 14, no. 1, p. 339-350-
dcterms.isPartOfEngineering applications of computational fluid mechanics-
dcterms.issued2020-
dc.identifier.isiWOS:000509930200001-
dc.identifier.scopus2-s2.0-85079229630-
dc.identifier.eissn1997-003X-
dc.description.validate202006 bcrc-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOSen_US
dc.description.pubStatusPublisheden_US
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