Sürdürülebilir Kalkınma Amaçları
Yayınlar
- MakaleYalnız künye2012Artificial neural networks for estimation of temporal rate coefficient of equilibrium bar volume
Kankal, Murat; Kömürcü, Murat Ihsan; Yüksek, Ömer; Akpinar, Adem
Present study consists the growth of a bar profile caused by cross-shore sediment transport. This is especially on growth of bar volume (V) toward equilibrium bar volume (Veq). Three analysis methods being a power and linear regression analysis (PRA and LRA) and an Artificial Neural Network (ANN) analysis were performed to determine empirical temporal rate coefficient (α). Forty-two experimental data were used for training set and the rest of the experimental data were used for testing set in the ANN analysis. As the results of analyses, the smallest average relative and root mean square error (RMSE) computed for the ANN methods are 7.578% and 0.029, respectively. It has been obtained that the ANN analysis, which is used for determination of α coefficient, gives reasonable results. Finally, bar volumes were calculated by means of computed α values and compared with the results of experimental data.
- Derleme MakaleYalnız künye2011Development of hydropower energy in Turkey: The case of Coruh river basin
Akpinar, Adem; Kömürcü, Murat Ihsan; Kankal, Murat
The main objective in doing the present study is to investigate the sustainable development of hydropower plants in the Coruh river basin of Turkey, which is least problem river of Turkey in respect to international cooperation as compared with Turkey's other trans-boundary waters. Initial studies concerning the hydropower production potential in Coruh basin had been carried out by Turkish authorities in the late 1960s. Total installed power capacity and annual average energy generation of 37 dams and run of river (without storage) hydropower plants developed at various project stages by The Electrical Power Resources Survey and Development Administration (EiE) in Coruh basin are 3132.70 MW and 10.55 TWh/yr, respectively. Today, this generation value corresponds 6.45% of Turkey's energy consumption in 2006 while it meets 6.3% of total electricity energy production of Turkey which is equal to 167.9 TINh/yr in 2006. Besides, this potential developed at various project stages in Coruh basin will provide 24.1% of Turkey's hydroelectric energy generation being equal to 43.8 TWh/yr in 2006. (C) 2010 Elsevier Ltd. All rights reserved.
- MakaleYalnız künye2011Estimation of the carbon to nitrogen (C:N) ratio in compostable solid waste using artificial neural networks
Bayram, Adem; Kankal, Murat; Ozsahin, Talat Sukru; Saka, Fatih
Organic waste constitutes a majority of all municipal solid waste (MSW), a fact which yields some unfavorable results at open dumps, sanitary landfills, and incineration plants. As part of an integrated solid waste management strategy, composting could be applied to mixed collected MSWs or separately collected leaves, food, and yard wastes. The factor most crucial to successful composting is the carbon to nitrogen (C:N) ratio of the waste. This study employs two predictive models to estimate the C:N ratio of compostable MSW, an artificial neural network (ANN) and multiple linear regression (MLR). These models are based on 52 solid waste samples taken from the MSW open dumping area in Gümüşhane Province, Turkey. To estimate the C:N ratio, seven predictive variables were adopted. The proportions of food and yard (F&Y), and ash and scoria (A&S) waste; the moisture content (MC), the fixed carbon (FC) content, the total amount of organic matter (TOM), high calorific value (HCV), and pH. Fortytwo of the samples were used for training, and the remaining ten were used to test the models. The average relative error attained by the best ANN was 6.376%, while that attained by the MLR model was 11.002%. The effects of TOM content, F&Y percentage, and A&S percentage on the C:N ratio were investigated by running the ANN model for a range of input variables.
- MakaleYalnız künye2011Modeling and forecasting of Turkey's energy consumption using socio-economic and demographic variables
Kankal, Murat; Akpinar, Adem; Kömürcü, Murat Ihsan; Özşahin, Talat Şükrü
This study deals with the modeling of the energy consumption in Turkey in order to forecast future projections based on socio-economic and demographic variables (gross domestic product-GDP, population, import and export amounts, and employment) using artificial neural network (ANN) and regression analyses. For this purpose, four diverse models including different indicators were used in the analyses. As the result of the analyses, this research proposes Model 2 as a suitable ANN model (having four independent variables being GDP, population, the amount of import and export) to efficiently estimate the energy consumption for Turkey. The proposed model predicted the energy consumption better than the regression models and the other three ANN models. Thus, the future energy consumption of Turkey is calculated by means of this model under different scenarios. The predicted forecast results by ANN were compared with the official forecasts. Finally, it was concluded that all the scenarios that were analyzed gave lower estimates of the energy consumption than the MENR projections and these scenarios also showed that the future energy consumption of Turkey would vary between 117.0 and 175.4. Mtoe in 2014.