A neural network-based approach for the prediction of urban SO₂ concentrations in the Istanbul metropolitan area
by Atilla Akkoyunlu, Kaan Yetilmezsoy, Ferruh Erturk, Ercan Oztemel
International Journal of Environment and Pollution (IJEP), Vol. 40, No. 4, 2010

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.

Online publication date: Tue, 23-Feb-2010

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