Publication:
Application of trend analysis and artificial neural networks methods: The case of Sakarya River

dc.contributor.authorÇeribaşı, Gökhan
dc.contributor.authorDoğan, Emrah
dc.contributor.authorAkkaya, Uğur
dc.contributor.buuauthorKocamaz, Uğur Erkin
dc.contributor.departmentKaracabey Meslek Yüksekokulu
dc.contributor.departmentBilgisayar Teknolojisi Bölümü
dc.contributor.orcid0000-0003-1172-9465
dc.contributor.scopusid55549566400
dc.date.accessioned2023-04-03T10:17:23Z
dc.date.available2023-04-03T10:17:23Z
dc.date.issued2016-09-05
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.
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.
dc.identifier.endpage999
dc.identifier.issn1026-3098
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85029029402
dc.identifier.startpage993
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.identifier.volume24
dc.identifier.wos000405882300011
dc.indexed.wosSCIE
dc.language.isoen
dc.publisherSharif University Technology
dc.relation.collaborationYurt içi
dc.relation.journalScientia Iranica
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectEngineering
dc.subjectTrend analysis
dc.subjectArtificial neural networks
dc.subjectSakarya river
dc.subjectRainfall
dc.subjectStream flow
dc.subjectSuspended load
dc.subjectTurkey
dc.subjectNeural networks
dc.subjectRain
dc.subjectRivers
dc.subjectANN modeling
dc.subjectAnnual average
dc.subjectAnnual rainfall
dc.subjectArtificial intelligence techniques
dc.subjectMonthly rainfalls
dc.subjectArtificial intelligence
dc.subjectSuspended loads
dc.subjectArtificial neural network
dc.subjectData processing
dc.subject.scopusChina; Penman-Monteith Equation; Trend Analysis
dc.subject.wosEngineering, multidisciplinary
dc.titleApplication of trend analysis and artificial neural networks methods: The case of Sakarya River
dc.typeArticle
dc.wos.quartileQ4
dspace.entity.typePublication
local.contributor.departmentKaracabey Meslek Yüksekokulu/Bilgisayar Teknolojisi Bölümü
local.indexed.atScopus
local.indexed.atWOS

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