Açık Akademik Arşiv Sistemi

Modeling biological oxygen demand of the Melen River in Turkey using an artificial neural network technique

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dc.contributor.authors Dogan, E; Sengorur, B; Koklu, R;
dc.date.accessioned 2020-02-26T08:57:54Z
dc.date.available 2020-02-26T08:57:54Z
dc.date.issued 2009
dc.identifier.citation Dogan, E; Sengorur, B; Koklu, R; (2009). Modeling biological oxygen demand of the Melen River in Turkey using an artificial neural network technique. JOURNAL OF ENVIRONMENTAL MANAGEMENT, 90, 1235-1229
dc.identifier.issn 0301-4797
dc.identifier.uri https://doi.org/10.1016/j.jenvman.2008.06.004
dc.identifier.uri https://hdl.handle.net/20.500.12619/50327
dc.description.abstract Artificial neural networks (ANNs) are being used increasingly to predict and forecast water resources' variables. The feed-forward neural network modeling technique is the most widely used ANN type in water resources applications. The main purpose of the study is to investigate the abilities of an artificial neural networks' (ANNs) model to improve the accuracy of the biological oxygen demand (BOD) estimation. Many of the water quality variables (chemical oxygen demand, temperature, dissolved oxygen, water flow, chlorophyll a and nutrients, ammonia, nitrite, nitrate) that affect biological oxygen demand concentrations were collected at 11 sampling sites in the Melen River Basin during 2001-2002. To develop an ANN model for estimating BOD, the available data set was partitioned into a training set and a test set according to station. in order to reach an optimum amount of hidden layer nodes, nodes 2, 3, 5, 10 were tested. Within this range, the ANN architecture having 8 inputs and 1 hidden layer with 3 nodes gives the best choice. Comparison of results reveals that the ANN model gives reasonable estimates for the BOD prediction. (c) 2008 Elsevier Ltd. All rights reserved.
dc.language English
dc.publisher ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
dc.subject Environmental Sciences & Ecology
dc.title Modeling biological oxygen demand of the Melen River in Turkey using an artificial neural network technique
dc.type Article
dc.identifier.volume 90
dc.identifier.startpage 1229
dc.identifier.endpage 1235
dc.contributor.department Sakarya Üniversitesi/Mühendislik Fakültesi/İnşaat Mühendisliği Bölümü
dc.contributor.saüauthor Doğan, Emrah
dc.contributor.saüauthor Şengörür, Bülent
dc.contributor.saüauthor Köklü, Rabia
dc.relation.journal JOURNAL OF ENVIRONMENTAL MANAGEMENT
dc.identifier.wos WOS:000261895500061
dc.identifier.doi 10.1016/j.jenvman.2008.06.004
dc.identifier.eissn 1095-8630
dc.contributor.author Doğan, Emrah
dc.contributor.author Şengörür, Bülent
dc.contributor.author Köklü, Rabia


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