Browsing by Author "Kivrak, E"
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Article Citation Count: 45A neural network-based approach for calculating dissolved oxygen profiles in reservoirs(Springer London Ltd, 2003) Şentürk, Emine; Karaer, F; Gürbüz, H; Kivrak, E; Sentürk, E; Yazici, A; Department of Modern LanguagesA Neural Network (NN) modelling approach has been shown to be successful in calculating pseudo steady state time and space dependent Dissolved Oxygen (DO) concentrations in three separate reservoirs with different characteristics using limited number of input variables. The Levenberg-Marquardt algorithm was adopted during training. Pre-processing before training and post processing after simulation steps were the treatments applied to raw data and predictions respectively. Generalisation was improved and over-fitting problems were eliminated: Early stopping method was applied for improving generalisation. The correlation coefficients between neural network estimates and field measurements were as high as 0.98 for two of the reservoirs with experiments that involve double layer neural network structure with 30 neurons within each hidden layer. A simple one layer neural network structure with 11 neurons has yielded comparable and satisfactorily high correlation coefficients for complete data set, and training, validation and test sets of the third reservoir.Conference Object Citation Count: 25Predicting dominant phytoplankton quantities in a reservoir by using neural networks(Springer, 2003) Gurbuz, H; Kivrak, E; Soyupak, S; Yerli, SVThe Levenberg-Marquardt algorithm was used to train artificial neural networks to predict the abundance of Cyclotella ocellata Pant. and Cyclotella kutzingiana Thwaites using time, depth, temperature, pH, dissolved oxygen, and electrical conductivity as input parameters for the oligo-mesotrophic Kuzgun Dam Reservoir, Turkey. The data were collected in monthly intervals during two ice-free seasons: between April 2000-November 2000 and April 2001-November 2001. To reduce over-fitting of the neural network based models, we employed single hidden layer networks with early stopping of training. Correlation coefficients, of neural network predictions with measurements of abundance of Cyclotella ocellata Pant. and Cyclotella kutzingiana Thwaites were 0.88 and 0.86, respectively.Article Citation Count: 5Seasonal changes in phytoplankton community structure in a high mountain reservoir, Kuzgun reservoir, Turkey(Taylor & Francis inc, 2004) Gürbüz, H; Kivrak, E; Soyupak, SSeasonal changes in phytoplankton community structure of Kuzgun reservoir, a high mountain reservoir, were studied during the ice-free period in 2000 and 2001. Bacillariophyta was the dominant group, followed by Chlorophyta and Dinophyta. The dominant species were Synedra delicatissima, Asterionella formosa, Fragilaria crotonensis, Cyclotella kidzingiana, Cyclotella ocellata, Oocystis borgei, Staurastrum longiradiatum, Ankistrodesmus falcatus, Ceratium hirundinella, and Peridinium cinctum. Maximum phytoplankton density was observed in late spring.