Title: A neural network-based approach for the prediction of urban SO2 concentrations in the Istanbul metropolitan area
Authors: Atilla Akkoyunlu, Kaan Yetilmezsoy, Ferruh Erturk, Ercan Oztemel
Addresses: Faculty of Engineering, Civil Engineering, Bogazici University, 34342, Bebek, Istanbul, Turkey. ' Faculty of Civil Engineering, Department of Environmental Engineering, Yildiz Technical University, 34220, Davutpasa Campus, Esenler, Istanbul, Turkey. ' Faculty of Civil Engineering, Department of Environmental Engineering, Yildiz Technical University, 34220, Davutpasa Campus, Esenler, Istanbul, Turkey. ' Faculty of Engineering, Department of Industrial Engineering, Marmara University, 34722, Goztepe Campus, Kadikoy, Istanbul, Turkey
Abstract: A three-layer Artificial Neural Network (ANN) model was developed to forecast air pollution levels. The subsequent SO2 concentration (24-hour averaged) being the output parameter of this study was estimated by seven input parameters such as preceding SO2 concentrations (24-hour averaged), average daily temperature, sea-level pressure, relative humidity, cloudiness, average daily wind speed and daily dominant wind direction. After Backpropagation training combined with Principal Component Analysis (PCA), the proposed model predicted subsequent SO2 values based on measured data. ANN testing outputs were proven to be satisfactory with correlation coefficients of about 0.770, 0.744 and 0.751 for the winter, summer and overall data, respectively.
Keywords: ANN; artificial neural networks; backpropagation algorithm; modelling; meteorological data; SO2; air pollution; urban air quality; Turkey; Istanbul; sulfur dioxide; sulphur dioxide; pollution prediction.
International Journal of Environment and Pollution, 2010 Vol.40 No.4, pp.301 - 321
Available online: 23 Feb 2010 *Full-text access for editors Access for subscribers Purchase this article Comment on this article