Potential assessment of the "support vector machine" method in forecasting ambient air pollutant trends.
Monitoring and forecasting of air quality parameters are popular and important topics of atmospheric and environmental research today due to the health impact caused by exposing to air pollutants existing in urban air. The accurate models for air pollutant prediction are needed because such models would allow forecasting and diagnosing potential compliance or non-compliance in both short- and long-term aspects. Artificial neural networks (ANN) are regarded as reliable and cost-effective method to achieve such tasks and have produced some promising results to date. Although ANN has addressed more attentions to environmental researchers, its inherent drawbacks, e.g., local minima, over-fitting training, poor generalization performance, determination of the appropriate network architecture, etc., impede the practical application of ANN. Support vector machine (SVM), 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 in this paper aims to examine the feasibility of applying SVM to predict air pollutant levels in advancing time series based on the monitored air pollutant database in Hong Kong downtown area. At the same time, the functional characteristics of SVM are investigated in the study. The experimental comparisons between the SVM model and the classical radial basis function (RBF) network demonstrate that the SVM is superior to the conventional RBF network in predicting air quality parameters with different time series and of better generalization performance than the RBF model.
Lu
Wei-Zhen
WZ
Department of Building and Construction, City University of Hong Kong, 83, Tat Chee Avenue, Kowloon Tong, Hong Kong. bcwzlu@cityu.edu.hk
Wang
Wen-Jian
WJ
D000393:Air Pollutants / Q000032:analysis*; D003198:Computer Simulation; D005544:Forecasting / Q000379:methods*; D006723:Hong Kong; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D001335:Vehicle Emissions
D000393:Air Pollutants; D001335:Vehicle Emissions