Journal
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
Volume 25, Issue 6, Pages 1246-1258Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.engappai.2011.10.013
Keywords
PM10 prediction; Wavelet transformation; Neural networks; Ensemble of predictors
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Funding
- Polish Ministry of Science and Higher Education
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The paper presents the application of wavelet transformation and neural network ensemble to the accurate forecasting of the daily average concentration of particulate matter of diameter up to 10 mu m (PM10). Few neural predictors are applied: the multilayer perceptron, radial basis function, Elman network and support vector machine as well as one linear ARX model. They are used for prediction in combination with wavelet decomposition, forming many individual prediction results that will be combined in an ensemble. The important role in presented approach fulfills the wavelet transformation and the integration of this ensemble. We have proposed solution applying the additional neural network responsible for the final forecast (integration of all particular prediction results). The numerical experiments for prediction of the daily concentration of the PM10 pollution in Warsaw are presented. They have shown good overall accuracy of prediction in terms of all investigated measures of quality. (C) 2011 Elsevier Ltd. All rights reserved.
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