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Article Citation - WoS: 44Citation - Scopus: 49A Neural Network-Based Approach for Calculating Dissolved Oxygen Profiles in Reservoirs(Springer London Ltd, 2003) Soyupak, S; Karaer, F; Gürbüz, H; Kivrak, E; Sentürk, E; Yazici, AA 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.Article Citation - Scopus: 1Adaptive Neuro-Fuzzy Inference Technique for Estimation of Light Penetration in Reservoirs(Springer Japan Kk, 2007) Soyupak, Selcuk; Karaer, Feza; Senturk, Engin; Hekim, Huseyin; Şntürk, EnginAn adaptive neuro-fuzzy inference technique has been adopted to estimate light levels in a reservoir. The data were collected randomly from Doganci Dam Reservoir over a number of years. The input data set is a matrix with vectors of time, depth, sampling location, and incident solar radiation. The output data set is a vector representing light measured at various depths. Randomization and logarithmic transformations have been applied as preprocessing. One-half of the data have been utilized for training; testing and validation steps utilized one-fourth each. An adaptive neuro-fuzzy inference system (ANFIS) has been built as a prediction model for light penetration. Very high correlation values between predictions and real values on light measurements with relatively low root mean square error values have been obtained for training, test, and validation data sets. Elimination of the overtraining problem was ensured by satisfying close root mean square error values for all sets.Article Citation - WoS: 4Citation - Scopus: 6An Automata Networks Based Preprocessing Technique for Artificial Neural Network Modelling of Primary Production Levels in Reservoirs(Elsevier, 2007) Kilic, Hurevren; Soyupak, Selcuk; Tuzun, Ilhami; Ince, Ozlem; Basaran, GokbenPrimary 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.Article Citation - WoS: 26Citation - Scopus: 31Impact Assessment of Different Management Scenarios on Water Quality of Porsuk River and Dam System - Turkey(Springer, 2005) Muhammetoglu, A; Muhammetoglu, H; Oktas, S; Ozgokcen, L; Soyupak, SPorsuk Dam Reservoir (PDR), which is located on Porsuk River, is the main drinking water resource of Eskisehir City-Turkey. Both the river and the reservoir are under the threat of several domestic and industrial point sources and land-based diffuse pollution. The river water quality is very poor with high concentrations of nitrogen and phosphorus compounds at the entrance to Porsuk Reservoir. The reservoir shows symptoms of a hypertrophic lake. The expected responses of the whole river and reservoir system under different pollution control scenarios were estimated to develop plausible water quality management strategies. The adopted scenarios assumed different levels of treatment for the major domestic point sources that include conventional treatment and tertiary treatment. The contemporary Turkish Allowable Discharge Limits (ADLs) and the best available technology choices were the investigated treatment options for the major industries. The expected improvements of water quality characteristics under the management scenario options have been estimated by means of mathematical models. The model choices were the QUAL2E for the river and BATHTUB for the reservoir. Recommendations for different levels of treatment were derived in order to improve the water quality both within the river and in the reservoir.
