Neural predictors and wavelet transformation for forecasting the PM10 pollution

Abstract

Purpose

The aim of this paper is to develop the accurate computer method of predicting the average PM10 pollution for the next day on the basis of some measured atmospheric parameters, like temperature, humidity, wind, etc. This method should be universal and applicable for any place under consideration.

Design/methodology/approach

The paper presents the new approach to the accurate forecasting of the daily average concentration of PM10. It is based on the application of the ensemble of neural networks and wavelet transformation of the time series, representing PM10 pollution.

Findings

On the basis of numerical experiments, the paper finds that application of many neural predictors cooperating with each other can significantly improve the quality of results. The paper shows that the developed forecasting system checked on the data of PM10 pollution in Warsaw generated good overall accuracy of prediction in terms of root mean squared error, mean absolute error and mean absolute percentage error.

Originality/value

The main novelty of the proposed approach is the application of the wavelet transformation and many neural networks organized in the form of ensemble. The individual neural predictors are integrated into one forecasting system using different forms of integrations, including the blind source separation method and neural‐based integration.

Citation

Siwek, K., Osowski, S. and Sowinski, M. (2011), "Neural predictors and wavelet transformation for forecasting the PM10 pollution", COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, Vol. 30 No. 4, pp. 1376-1388. https://doi.org/10.1108/03321641111133262