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Article Citation - WoS: 14Citation - Scopus: 14Fuzzy Logic Model To Estimate Seasonal Pseudo Steady State Chlorophyll-A Concentrations in Reservoirs(Springer, 2004) Soyupak, S; Chen, DGA fuzzy logic model is developed to estimate pseudo steady state chlorophyll-a concentrations in a very large and deep dam reservoir, namely Keban Dam Reservoir, which is also highly spatial and temporal variable. The estimation power of the developed fuzzy logic model was tested by comparing its performance with that from the classical multiple regression model. The data include chlorophyll-a concentrations in Keban lake as a response variable, as well as several water quality variables such as PO4 phosphorus, NO3 nitrogen, alkalinity, suspended solids concentration, pH, water temperature, electrical conductivity, dissolved oxygen concentration and Secchi depth as independent environmental variables. Because of the complex nature of the studied water body, as well as non-significant functional relationships among the water quality variables to the chlorophyll-a concentration, an initial analysis is conducted to select the most important variables that can be used in estimating the chlorophyll-a concentrations within the studied water body. Following the outcomes from this initial analysis, the fuzzy logic model is developed to estimate the chlorophyll-a concentrations and the advantages of this new model is demonstrated in model fitting over the traditional multiple regression method.Conference Object Citation - WoS: 22Citation - Scopus: 21Predicting 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 - 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.
