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Öğe A COMBINED WAVELET AND NEURAL NETWORK MODEL FOR FORECASTING STREAMFLOW DATA(PARLAR SCIENTIFIC PUBLICATIONS (P S P), 2016) Yarar, Alpaslan; Onucyildiz, Mustafa; Copty, Nadim K.The modeling of streamflow is often needed for the sustainable management of water resources and for the protection against flooding. Over the years numerous streamflow forecasting models have been developed, black-box models, like Artificial Neural Networks (ANN), have became quite popular in the field of hydrologic engineering, because of their rapidity and less data requirements compared to physics-based models. In this study, a hybrid model, Wavelet-Neural Network (WNN), for the prediction of streamflow is developed. The model incorporates ANN and wavelet transform for the analysis of variations in streamflow time series. For demonstration, the model is applied to streamflow data from four flow observation stations (FOS), located in the West Mediterranean Basin of Turkey. Monthly mean streamflow data from the four FOS were used in the model. Original series were decomposed sub-series by wavelet transform. These sub-series were used for ANN model. In order to evaluate the performance of the WNN model, a multi regression (MR) model was also developed based on the same data set. Results show that WNN model forecasts the streamflow more accurately than the MR model with correlations between estimated and observed streamflow data ranging from 0.84-0.88.Öğe EVALUATION OF DECENTRALIZED AND CENTRALIZED WASTEWATER TREATMENT PLANTS(INT SCIENTIFIC CONFERENCE SGEM, 2008) Onucyildiz, Mustafa; Sevimli, Mehmet Faik; Gorgulu, GkvencIn this study, it is aimed to find out which wastewater treatment plant (WWTP) is more economical by means of comparison of initial investment costs and operational costs of decentralized and centralized domestic wastewater treatment plants in and around Antalya which is located in Mediterranean Region. For this purpose, data of 14 decentralized and 5 centralized domestic wastewater treatment plants is evaluated. The design flowrates of decentralized and centralized WWTP are varying 120-900 m(3) day(-1) and 5000-22000 m(3) day(-1) respectively. As the flowrate capacity of WWTP increases, the initial investment cost and also operational cost for the treatment of per m 3 of wastewater decreases. The results show us that the mean cost of I in 3 wastewater treatment is about 0.17 $m(-3) in the decentralized wastewater treatment plants and 0.10 $m(-3) in the centralized wastewater treatment plants. This means that, collecting and transferring the wastewater to the central domestic wastewater treatment plants and making the treatment process in these plants are more economical than decentralized domestic wastewater treatment plants.Öğe FORECASTING THE RAINFALL DATA BY ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM(INT SCIENTIFIC CONFERENCE SGEM, 2009) Yarar, Alpaslan; Onucyildiz, Mustafa; Sevimli, M. FaikKonya is the biggest city of Turkey in terms of area and agricultural land, on the other hand sixth biggest city in terms of population. Because of the decrease of rainfall and increase in temperature, the agricultural production and daily water consumption are effected negatively in last years. Rainfall, one of the basic parameters of the hydrological cycle, has a big importance to determine the water budgets and to improve the water supply policy. In this study, monthly total rainfall data belong to Konya between 1970-2002 years, have been studied to forecast by Adaptive Neuro-Fuzzy Inference System (ANFIS). And model's performance has been evaluated by comparison with the Lineer Regression (LR) as one of the traditional methods.Öğe Modelling level change in lakes using neuro-fuzzy and artificial neural networks(ELSEVIER SCIENCE BV, 2009) Yarar, Alpaslan; Onucyildiz, Mustafa; Copty, Nadim K.Accurate estimation of level change in lakes and reservoirs in response to climatic variations is an important step for the development of sustainable water usage policies, particularly for complex hydrological systems such as Lake Beysehir, Turkey. In this study, level changes of Lake Beysehir were estimated using adaptive neuro-fuzzy inference system (ANFIS), artificial neural networks (ANN) and a seasonal autoregressive integrated moving average (SARIMA). The ANN and ANFIS models were first trained based on observed data between 1966 and 1984, and then used to predict water level changes over the test period extending from 1985 to 1990. The performances of the different models were evaluated by comparing the corresponding values of mean squared errors (MSE) and decisive coefficients (R-2). While all models produced acceptable results, the minimum MSE value (0.0057) and the maximum R-2 value (0.7930) were obtained with ANFIS model, followed by the three-layered artificial neural network model (ANN1). The lowest performance was observed with the SARIMA model. (c) 2008 Elsevier B.V. All rights reserved.Öğe Treatment of pesticide wastewater by physicochemical and Fenton processes(ASIAN JOURNAL OF CHEMISTRY, 2008) Ozdemir, Celalettin; Sahinkaya, Serkan; Onucyildiz, MustafaThe process of pesticide removal from industrial wastewater using which chemical, vacuum-chemical and Fenton's reactions have been analyzed. Fenton process is attractive alternative to conventional oxidation processes in effluent treatment of recalcitrant compounds. The aim of this study is to evaluate the efficiency of chemical, vacuum and Fenton processes for the reduction of chemical oxygen demand in wastewaters from pesticide industry. In this study wastewater from pesticide industry was used. Whereas in the chemical procedure [Ca(OH)(2) and KMnO4], the chemical oxygen demand removal efficiency is 94.9%; in the vacuum-Ca(OH)(2) + KMnO4 system (with 250 mg/L KMnO4, 1 mL H2SO4, 5 mg/L polyelectrolyte and 2000 mg/L CaOH application) this efficiency was 97.8%; and a 99.8% KOI removal efficiency was obtained by the Fenton process (the optimum ratio of [Fe2+] to [H2O2] was 1:1.56 (mM/mM), at pH 3.0).
