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

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dc.contributor.authors Ceribasi, G; Dogan, E; Akkaya, U; Kocamaz, UE;
dc.date.accessioned 2020-03-06T08:07:44Z
dc.date.available 2020-03-06T08:07:44Z
dc.date.issued 2017
dc.identifier.citation Ceribasi, G; Dogan, E; Akkaya, U; Kocamaz, UE; (2017). Application of trend analysis and artificial neural networks methods: The case of Sakarya River. SCIENTIA IRANICA, 24, 999-993
dc.identifier.issn 1026-3098
dc.identifier.uri https://hdl.handle.net/20.500.12619/67182
dc.description.abstract Various 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. (C) 2017 Sharif University of Technology. All rights reserved.
dc.language English
dc.publisher SHARIF UNIV TECHNOLOGY
dc.subject Engineering
dc.title Application of trend analysis and artificial neural networks methods: The case of Sakarya River
dc.type Article
dc.identifier.volume 24
dc.identifier.startpage 993
dc.identifier.endpage 999
dc.contributor.department Sakarya Uygulamalı Bilimler Üniversitesi/Teknoloji Fakültesi/İnşaat Mühendisliği Bölümü
dc.contributor.saüauthor Çeribaşı, Gökmen
dc.contributor.saüauthor Doğan, Emrah
dc.relation.journal SCIENTIA IRANICA
dc.identifier.wos WOS:000405882300011
dc.contributor.author Çeribaşı, Gökmen
dc.contributor.author Doğan, Emrah
dc.contributor.author U. Akkaya
dc.contributor.author U. E. Kocamaz


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