Tuzun, IlhamiSoyupak, SelcukInce, OzlemBasaran, Gokben2024-10-062024-10-06200710972-99840973-7308[WOS-DOI-BELIRLENECEK-563]https://hdl.handle.net/20.500.14411/8706An 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.eninfo:eu-repo/semantics/closedAccessDissolved oxygenNeural networksReservoirsWater quality modelingLevenberg-Marquardt algorithmGeneralisationArticleN/A72521WOS:000420108300001