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dc.contributor.authorPartal, Turgay
dc.date.accessioned2020-06-21T13:32:47Z
dc.date.available2020-06-21T13:32:47Z
dc.date.issued2016
dc.identifier.issn1226-7988
dc.identifier.issn1976-3808
dc.identifier.urihttps://doi.org/10.1007/s12205-015-0556-0
dc.identifier.urihttps://hdl.handle.net/20.500.12712/13267
dc.descriptionWOS: 000378516700047en_US
dc.description.abstractThis paper investigates the comparative performance of wavelet based radial basis networks and multi linear regression in daily reference evapotranspiration estimation. The meteorological data (air temperature, solar radiation, wind speed, relative humidity) from two stations in the United States was evaluated for estimating models. The wavelet based radial basis network combines wavelet transformation and radial basis neural network, while the wavelet based regression model combines wavelet transformation and multi linear regression. The results show that the wavelet transformation has significantly positive effects on modeling performance. The wavelet based radial basis network provided the best performance evaluation criteria.en_US
dc.language.isoengen_US
dc.publisherKorean Society of Civil Engineers-Ksceen_US
dc.relation.isversionof10.1007/s12205-015-0556-0en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectwavelet transformationen_US
dc.subjectradial basis neural networken_US
dc.subjectfeed forward neural networken_US
dc.subjectmulti linear regressionen_US
dc.subjectevapotranspirationen_US
dc.subjectestimatingen_US
dc.titleComparison of Wavelet Based Hybrid Models for Daily Evapotranspiration Estimation using Meteorological Dataen_US
dc.typearticleen_US
dc.contributor.departmentOMÜen_US
dc.identifier.volume20en_US
dc.identifier.issue5en_US
dc.identifier.startpage2050en_US
dc.identifier.endpage2058en_US
dc.relation.journalKsce Journal of Civil Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US


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