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

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dc.contributor.authorTüzün, İlhami
dc.contributor.authorSoyupak, Selçuk
dc.contributor.authorİnce, Özlem
dc.contributor.authorBaşaran, Gökben
dc.date.accessioned2025-01-21T16:35:57Z
dc.date.available2025-01-21T16:35:57Z
dc.date.issued2007
dc.departmentKırıkkale Üniversitesi
dc.description.abstractAn 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.
dc.identifier.endpage21
dc.identifier.issn0972-9984
dc.identifier.issn0973-7308
dc.identifier.issue2
dc.identifier.startpage5
dc.identifier.urihttps://hdl.handle.net/20.500.12587/24206
dc.identifier.volume7
dc.identifier.wosWOS:000420108300001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.language.isoen
dc.publisherCentre Environment Social & Economic Research Publ-Ceser
dc.relation.ispartofInternational Journal of Ecology & Development
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241229
dc.subjectDissolved oxygen; Neural networks; Reservoirs; Water quality modeling; Levenberg-Marquardt algorithm; Generalisation
dc.titleApplication of Artificial Neural Network-Based Approach for Calculating Dissolved Oxygen Profiles in Kapulukaya Dam Reservoir
dc.typeArticle

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