THE FLOW-RATE PREDICTION IN ERGENE WATERSHED

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dc.authoridSevgen, Selcuk/0000-0003-1443-1779
dc.authorwosidSevgen, Selcuk/C-8026-2019
dc.contributor.authorBayrak, Gokcen
dc.contributor.authorSevgen, Selcuk
dc.contributor.authorSamli, Ruya
dc.date.accessioned2024-06-12T10:52:35Z
dc.date.available2024-06-12T10:52:35Z
dc.date.issued2021
dc.departmentTrakya Üniversitesien_US
dc.description.abstractThis paper presents an experimental study about prediction of the highest monthly average flow-rate of the Ergene River. Hydro-meteorological data from Luleburgaz Meteorology Station (MS) and Luleburgaz Flow Observation Station (FOS) have been used for prediction. Ergene watershed has point and non-point sources pollution and has seasonal floods. The study area is located in the middle of the watershed. First of all, hydro-meteorological data of all months between 1995 and 2017 were obtained from Luleburgaz FOS. After that, the relationship between the data were modeled by Artificial Neural Network (ANN), Multiple Linear Regression (MLR) and Support Vector Machine (SVM). Also, the monthly flow-rate of Ergene River Luleburgaz Station is predicted annually for the years 2017 and 2018. The results demonstrate that the ANN, MLR and SVM models can predict the flow-rate with high accuracy, but the ANN is the most appropriate model to the Ergene watershed data set.en_US
dc.description.sponsorshipScientific and Technical Research Council of Turkey [118E682, BYP-2019-33988]en_US
dc.description.sponsorshipThis work was supported by the Scientific and Technical Research Council of Turkey, under Project 118E682 and Research Fund of Istanbul University-Cerrahpasa under Project BYP-2019-33988.en_US
dc.identifier.doi10.26471/cjees/2021/016/175
dc.identifier.endpage303en_US
dc.identifier.issn1842-4090
dc.identifier.issn1844-489X
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85107662965en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage293en_US
dc.identifier.urihttps://doi.org/10.26471/cjees/2021/016/175
dc.identifier.urihttps://hdl.handle.net/20.500.14551/18764
dc.identifier.volume16en_US
dc.identifier.wosWOS:000653217400003en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherCarpathian Assoc Environment And Earth Sciencesen_US
dc.relation.ispartofCarpathian Journal Of Earth And Environmental Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectErgene Watersheden_US
dc.subjectFlow-Rate Predictionen_US
dc.subjectMultiple Linear Regressionen_US
dc.subjectSupport Vector Machineen_US
dc.subjectArtificial Neural-Networken_US
dc.subjectSupport Vector Regressionen_US
dc.subjectShort-Termen_US
dc.subjectRiver-Basinen_US
dc.subjectModelen_US
dc.subjectAnnen_US
dc.subjectManagementen_US
dc.titleTHE FLOW-RATE PREDICTION IN ERGENE WATERSHEDen_US
dc.typeArticleen_US

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