A neural network-based approach for the prediction of urban SO2 concentrations in the Istanbul metropolitan area

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 oil 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.