Evolving the ensemble of predictors model for forecasting the daily average PM10
by Krzysztof Siwek; Stanislaw Osowski; Mieczyslaw Sowinski
International Journal of Environment and Pollution (IJEP), Vol. 46, No. 3/4, 2011

Abstract: The paper develops the methods of accurately forecasting the daily average concentration of PM10. We apply the Support Vector Machine in the regression mode (SVR) as the main workhorse of prediction. Different approaches to the prediction are tried: the direct application of SVR, the combination of SVR and wavelet decomposition, and the Blind Source Separation (BSS) method for improving the final accuracy of prediction. The main novelty of the proposed approach is the application of the ensemble of predictors integrated using the BSS method. The numerical experiments of predicting the daily concentration of the PM10 pollution in Warsaw have shown good overall accuracy of prediction in terms of RMSE, MAE and MAPE errors, as well as correlation and index of agreement measures.

Online publication date: Fri, 17-Feb-2012

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Environment and Pollution (IJEP):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?


Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com