A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multilayer feedforward networks are universal approximators
Neural Networks
Ten lectures on wavelets
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Forecasting of the daily meteorological pollution using wavelets and support vector machine
Engineering Applications of Artificial Intelligence
Environmental Modelling & Software
Environmental Modelling & Software
Variations of PM10 Pollution Index in Shanghai during Recent 9 Years
CESCE '10 Proceedings of the 2010 International Conference on Challenges in Environmental Science and Computer Engineering - Volume 01
Ensemble canonical correlation analysis
Applied Intelligence
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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@mm (PM"1"0). 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 PM"1"0 pollution in Warsaw are presented. They have shown good overall accuracy of prediction in terms of all investigated measures of quality.