Please use this identifier to cite or link to this item: http://hdl.handle.net/11452/32141
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dc.contributor.authorÇeribaşı, Gökhan-
dc.contributor.authorDoğan, Emrah-
dc.contributor.authorAkkaya, Uğur-
dc.date.accessioned2023-04-03T10:17:23Z-
dc.date.available2023-04-03T10:17:23Z-
dc.date.issued2016-09-05-
dc.identifier.citationÇeribaşı, G. vd. (2017). ''Application of trend analysis and artificial neural networks methods: The case of Sakarya River''. Scientia Iranica, 24(3), 993-999.en_US
dc.identifier.issn1026-3098-
dc.identifier.urihttps://doi.org/10.24200/sci.2017.4082-
dc.identifier.urihttp://scientiairanica.sharif.edu/article_4082.html-
dc.identifier.urihttp://hdl.handle.net/11452/32141-
dc.description.abstractVarious artificial intelligence techniques are used in order to make prospective estimations with available data. The most common and applied method among these artificial intelligence techniques is Artificial Neural Networks (ANN). On the other hand, another method which is used in order to make prospective estimations with available data is Trend Analysis. When the relation of these two methods is analyzed, Artificial Neural Networks method can present the prospective estimation numerically, while there is no such a case in Trend Analysis. Trend Analysis method presents result of prospective estimation as a decrease or increase in data. Therefore, it is quite important to make a comparison between these methods which brings about prospective estimation with the available data, because these two methods are used in most of these studies. In this study, annual average stream flow and suspended load measured in Sakarya River along with average annual rainfall trend were analyzed with trend analysis method. Daily, weekly, and monthly average stream flows and suspended loads measured in Sakarya River and average daily, weekly, and monthly rainfall data of Sakarya were all analyzed by ANN Model. Results of trend analysis method and ANN model were compared.en_US
dc.language.isoenen_US
dc.publisherSharif University Technologyen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rightsAtıf Gayri Ticari Türetilemez 4.0 Uluslararasıtr_TR
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectEngineeringen_US
dc.subjectTrend analysisen_US
dc.subjectArtificial neural networksen_US
dc.subjectSakarya riveren_US
dc.subjectRainfallen_US
dc.subjectStream flowen_US
dc.subjectSuspended loaden_US
dc.subjectTurkeyen_US
dc.subjectNeural networksen_US
dc.subjectRainen_US
dc.subjectRiversen_US
dc.subjectANN modelingen_US
dc.subjectAnnual averageen_US
dc.subjectAnnual rainfallen_US
dc.subjectArtificial intelligence techniquesen_US
dc.subjectMonthly rainfallsen_US
dc.subjectArtificial intelligenceen_US
dc.subjectSuspended loadsen_US
dc.subjectArtificial neural networken_US
dc.subjectData processingen_US
dc.titleApplication of trend analysis and artificial neural networks methods: The case of Sakarya Riveren_US
dc.typeArticleen_US
dc.identifier.wos000405882300011tr_TR
dc.identifier.scopus2-s2.0-85029029402tr_TR
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergitr_TR
dc.contributor.departmentUludağ Üniversitesi/Karacabey Meslek Yüksekokulu/Bilgisayar Teknolojisi Bölümü.tr_TR
dc.contributor.orcid0000-0003-1172-9465tr_TR
dc.identifier.startpage993tr_TR
dc.identifier.endpage999tr_TR
dc.identifier.volume24tr_TR
dc.identifier.issue3tr_TR
dc.relation.journalScientia Iranicaen_US
dc.contributor.buuauthorKocamaz, Uğur Erkin-
dc.relation.collaborationYurt içitr_TR
dc.subject.wosEngineering, multidisciplinaryen_US
dc.indexed.wosSCIEen_US
dc.indexed.scopusScopusen_US
dc.wos.quartileQ4en_US
dc.contributor.scopusid55549566400tr_TR
dc.subject.scopusChina; Penman-Monteith Equation; Trend Analysisen_US
Appears in Collections:Scopus
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