Forecasting daily ambient air pollution based on least squares support vector machines

Meteorological and pollutions data are collected daily at monitoring stations of a city. This pollutant-related information can be used to build an early warning system, which provides forecast and also alarms health advice to local inhabitants by medical practicians and local government. In the literature, air quality or pollutant level predictive models using multi-layer perceptrons (MLP) have been employed at a variety of cities by environmental researchers. The practical applications of these models however suffer from different drawbacks so that good generalization may not be obtained. Least squares support vector machines (LS-SVM), a novel type of machine learning technique based on statistical learning theory, can be used for regression and time series prediction. LS-SVM can overcome most of the drawbacks of MLP and has been reported to show promising results.