Application of Artificial Neural Network-Based Approach for Calculating Dissolved Oxygen Profiles in Kapulukaya Dam Reservoir

dc.authorwosid tüzün, ilhami/A-4461-2018
dc.contributor.author Tuzun, Ilhami
dc.contributor.author Soyupak, Selcuk
dc.contributor.author Ince, Ozlem
dc.contributor.author Basaran, Gokben
dc.date.accessioned 2024-10-06T10:57:21Z
dc.date.available 2024-10-06T10:57:21Z
dc.date.issued 2007
dc.department Atılım University en_US
dc.department-temp [Tuzun, Ilhami; Ince, Ozlem; Basaran, Gokben] Kirikkale Univ, Fac Arts & Sci, Dept Biol, TR-71450 Yahsihan, Kirikkale, Turkey; [Soyupak, Selcuk] Atilim Univ, Fac Engn, Dept Civil Engn, TR-06836 Ankara, Turkey en_US
dc.description.abstract An Artificial Neural Network (ANN) modelling approach has been shown to be successful in calculating time and space dependent dissolved oxygen (DO) concentration profiles in Kapulukaya Dam Reservoir using limited number of input variables. The variation of inflow to the reservoir with respect to time was significantly high. The reservoir operational levels were relatively stable. The Levenberg-Marquardt algorithm was adopted during training. Preprocessing before training and post processing after simulation steps were the treatments applied to raw data and predictions respectively. Different configurations of Multilayer perceptron neural networks were designed by selecting different combinations of number of hidden layers (single and double) and number of neurons within each of the hidden layers. Generalisation was improved and over-fitting problems were eliminated: Early stopping method was applied for improving generalisation. The conventional model criteria of correlation coefficient (R) and mean square errors (MSE) were adopted to compare model performances. The correlation coefficients between neural network estimates and field measurements were as high as 0.96 for daily and monthly data respectively with experiments that involve double layer neural network structure with 31 neurons within each hidden layer. The study results revealed that the data sizes effect model performances up to a certain level. en_US
dc.description.woscitationindex Emerging Sources Citation Index
dc.identifier.citationcount 1
dc.identifier.endpage 21 en_US
dc.identifier.issn 0972-9984
dc.identifier.issn 0973-7308
dc.identifier.issue 2 en_US
dc.identifier.startpage 5 en_US
dc.identifier.uri https://hdl.handle.net/20.500.14411/8706
dc.identifier.volume 7 en_US
dc.identifier.wos WOS:000420108300001
dc.language.iso en en_US
dc.publisher Centre Environment Social & Economic Research Publ-ceser en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.subject Dissolved oxygen en_US
dc.subject Neural networks en_US
dc.subject Reservoirs en_US
dc.subject Water quality modeling en_US
dc.subject Levenberg-Marquardt algorithm en_US
dc.subject Generalisation en_US
dc.title Application of Artificial Neural Network-Based Approach for Calculating Dissolved Oxygen Profiles in Kapulukaya Dam Reservoir en_US
dc.type Article en_US
dc.wos.citedbyCount 1
dspace.entity.type Publication

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