Journal of Scientific & Industrial Research Vol. 65, February 2006, pp. 128-134 Artificial neural network modeling of methane emissions at Istanbul Kemerburgaz-Odayeri landfill site H Kurtulus Ozcan 1, *, Osman N Ucan 2 , Ulku Sahin 1 , Mehmet Borat 1 and Cuma Bayat 1 1 Istanbul University, Engineering Faculty, Environmental Eng. Dept. 34 320, Avcilar, Istanbul, Turkey 2 Istanbul University, Engineering Faculty, Electrical-Electronics Eng. Dept. 34 320, Avcilar, Istanbul, Turkey Received 29 April 2005; revised 13 October 2005; accepted 23 November 2005 This study reports on Istanbul Kemerburgaz-Odayeri (Turkey) solid waste landfills, models CH 4 , CO 2 , CO, atmospheric temperature parameters of this area, and predicts CH 4 using Artificial Neural Networks (ANN). Here, ANN structure employs 4 input, 10 hidden and 1 output neurons. In order to evaluate performance of ANN model, statistical performance indices between real and estimated data have been measured and the correlation is found as 0.983 and 0.806 for training and testing respectively. Keywords: Artificial neural network, Carbon dioxide, Istanbul, Landfill gas, Methane IPC Code: G06N3/02; C02F11/04 Introduction Solid waste landfills are quite different from one another due to their heterogenic structures and one cannot find a single equation of decomposition rate or constant because there exist many decomposable matters in landfill 1 . Biological decomposition results in the landfill gases production. Initially, decomposition process is aerobic due to the existence of oxygen. When there is no oxygen left, anaerobic conditions arise and the organic components decompose after a chain of reactions 2 . Some factors that greatly affect the biological decomposition of solid wastes are, the nutrient ingredients of the waste, temperature, moisture, pH, particle dimensions, density and the composition of buried wastes 1,3,4 . In landfill gases (CH 4 , CO 2 , CO, H 2 S, N 2 , NH 3 , and O 2 ), CH 4 (60%) and CO 2 (40%) are major gases, resulting from the anaerobic degradation of degradable domestic solid wastes 2,5,6 . Methane reduction should be a major objective in any mitigation strategy as emissions need only be reduced by 10-15 percent to stabilize the global atmospheric burden, while CO 2 emissions would have to be reduced by 60-80 percent to achieve stabilization 7 . Landfills contributed a portion of the total increase in the atmospheric concentration of CH 4 (1 % per year) for 1984-1992 8 . As a landfill gases, CH 4 depicts explosive properties even at low concentration (5-15 %) in air. In O 2 limited case, even if CH 4 reaches these concentration values, the risk of explosion is highly decreased 9 . Artificial Neural Network (ANN) is used in various engineering fields and, demonstrated remarkable success 10 . ANN models are computer programs that are designed to emulate human knowledge processing, speech, prediction, classification, and control 11 . ANN is a cellular information processing system designed and developed on the basis of the perceived notion of the human brain and its neural system 12 . In air pollution modeling, neural network (NN)-based models have been applied to predict various pollutant concentrations. Chelani et al 13 constructed a three-layer NN model with a hidden recurrent layer to predict SO 2 concentration at three sites in Delhi. In their study, a multivariate regression model was also used for comparison with the results obtained by using NN model. Sahin et al 14 applied Multi-Layer Perceptron NN model to predict daily CO concentrations using meteorological variables as predictors for the European part of Istanbul, Turkey. Viotti et al 15 used ANN to forecast short and middle long-term concentration levels for Benzene, NOx, CO and ozone. Abdul-Wahab & Al-Alawi 11 applied NN to predict ozone concentrations as a function of meteorological conditions and various air quality parameters. The results of their study indicate that the ANN is a promising method for air pollution _________________ *Author for correspondence Tel: +90 212 4737070/17726; Fax: +90 212 4737180 E-mail: hkozcan@istanbul.edu.tr