Inderscience Publishers

Evolving the ensemble of predictors model for forecasting the daily average PM10

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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.

Keywords: PM10 forecasting, support vector machines, SVM, ensemble, predictor modelling, wavelet decomposition, blind source separation, BSS, environmental pollution, air pollution, air quality

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