Browsing by Author "Basaran, Gokben"
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Article Citation Count: 1Application of Artificial Neural Network-Based Approach for Calculating Dissolved Oxygen Profiles in Kapulukaya Dam Reservoir(Centre Environment Social & Economic Research Publ-ceser, 2007) Tuzun, Ilhami; Soyupak, Selcuk; Ince, Ozlem; Basaran, GokbenAn 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.Article Citation Count: 4An automata networks based preprocessing technique for artificial neural network modelling of primary production levels in reservoirs(Elsevier, 2007) Kılıç, Hürevren; Soyupak, Selcuk; Tuzun, Ilhami; Ince, Ozlem; Basaran, Gokben; Computer EngineeringPrimary production in lakes and reservoirs develops as a result of complex reactions and interactions. Artificial neural networks (ANN) emerges as an approach in quantification of primary productivity in reservoirs. Almost all of the past ANN applications employed input data matrices whose vectors represent either water quality parameters or environmental characteristics. Most of the time, the components of input matrices are determined using expert opinion that implies possible factors that affect output vector. Major disadvantage of this approach is the possibility of ending-up with an input matrix that may have high correlations between some of its components. In this paper, an automata networks (AN) based preprocessing technique was developed to select suitable and appropriate constituents of input matrix to eliminate redundancy and to enhance calculation efficiency. The proposed technique specifically provides an apriori rough behavioral modeling through identification of minimal AN interaction topology. Predictive ANN models of primary production levels were developed for a reservoir following AN based pre-modeling step. The achieved levels of model precisions and performances were acceptable: the calculated root mean square error values (RMSE) were low; a correlation coefficient (R) as high as 0.83 was achieved with an ANN model of a specific structure. (c) 2006 Elsevier B.V. All rights reserved.