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Journal of Control Science and Engineering
Volume 2012 (2012), Article ID 518032, 11 pages
Research Article

Short-Term Prediction of Air Pollution in Macau Using Support Vector Machines

1Department of Computer and Information Science, University of Macau, Macau
2Faculty of Science and Technology, University of Macau, Macau
3Department of Electromechanical Engineering, University of Macau, Macau

Received 16 January 2012; Accepted 29 March 2012

Academic Editor: Qingsong Xu

Copyright © 2012 Chi-Man Vong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Forecasting of air pollution is a popular and important topic in recent years due to the health impact caused by air pollution. It is necessary to build an early warning system, which provides forecast and also alerts health alarm to local inhabitants by medical practitioners and the local government. Meteorological and pollutions data collected daily at monitoring stations of Macau can be used in this study to build a forecasting system. Support vector machines (SVMs), a novel type of machine learning technique based on statistical learning theory, can be used for regression and time series prediction. SVM is capable of good generalization while the performance of the SVM model is often hinged on the appropriate choice of the kernel.