Forecasting of the daily meteorological pollution using wavelets and support vector machine

The paper presents the method of daily air pollution forecasting by using support vector machine (SVM) and wavelet decomposition. Based on the observed data of NO2, CO, SO2 and dust, for the past years and actual meteorological parameters, like wind, temperature, humidity and pressure, we propose the forecasting approach, applying the neural network of SVM type, working in the regression mode. To obtain the acceptable accuracy of forecast we decompose the measured time series data into wavelet representation and predict the wavelet coefficients. On the basis of these predicted values the final forecasting is prepared. The paper presents the results of numerical experiments on the basis of the measurements made by the meteorological stations, situated in the northern region of Poland. (C) 2006 Elsevier Ltd. All rights reserved.