Air pollutant parameter forecasting using support vector machines

Forecasting of air quality parameters is an important topic of atmospheric and environmental research today due to the health impact caused by airborne pollutants existing in urban areas. The support vector machine (SVM), as a novel type of learning machine based on statistical learning theory, can be used for regression and time series prediction and have been reported to perform well by some promising results. The work presented here examines the feasibility of applying SVM to predict pollutant concentrations. In the meantime, the functional characteristics of the SVM are also investigated in the study. The experimental comparison between the SVM and the classical radial basis function (RBF) network demonstrates that the SVM is superior to conventional RBF in predicting air quality parameters with different time series.