Air pollutant parameter forecasting using support vector machines
Author(s)
Author(s)
Lu, W. Z.
Wang, W. J.
Xu, Z. B.
Lo, S. M.
Fan, H. Y.
Date Issued
2002
Publisher
IEEE
Related Publication(s)
Proceedings of the 2002 International Joint Conference on Neural Networks (IJCNN'02)
Start page
630
End page
635
Abstract
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 has been reported to perform well with some promising results. The work presented examines the feasibility of applying SVM to predict pollutant concentrations. The functional characteristics of the SVM are also investigated. 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.
SFU Affiliated Publication
No
Availability at SFU Library
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