Prediction of air pollutant levels using support vector machines: An effective tool
Author(s)
Author(s)
Lu, W. Z.
Wang, W. J.
Wang, X. K.
Date Issued
2003
Conference
The 7th International Conference on the Application of Artificial Intelligence to Civil and Structural Engineering
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 have been reported to perform well by some promising results. The work presented here aims to examine 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.
SFU Affiliated Publication
No
Availability at SFU Library
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