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Neural predictor ensemble for accurate forecasting of PM10 pollution

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3 Author(s)
Siwek, K. ; Warsaw Univ. of Technol., Warsaw, Poland ; Osowski, S. ; Sowinski, M.

The paper presents the neural network approach to the accurate forecasting of the daily average concentration of PM10. Few neural predictors are applied: the multilayer perceptron, radial basis function, Elman network and support vector machine. They are used for prediction either in direct application or 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 integration of this ensemble. We have proposed solution applying the principal component analysis and additional neural network responsible for final forecast. 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: the errors RMSE, MAE MAPE as well as index of agreement and correlation of the prediction and true values.

Published in:

Neural Networks (IJCNN), The 2010 International Joint Conference on

Date of Conference:

18-23 July 2010

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