Time-series forecasting of pollutant concentration levels using particle swarm optimization and artificial neural networks

This study evaluates the application of an intelligent hybrid system for time-series forecasting of atmospheric pollutant concentration levels. The proposed method consists of an artificial neural network combined with a particle swarm optimization algorithm. The method not only searches relevant ti...

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Bibliographic Details
Published in:Química Nova 2013, Vol.36 (6), p.783-789
Main Authors: Albuquerque Filho, Francisco S. de, Madeiro, Francisco, Fernandes, Sérgio M. M., Mattos Neto, Paulo S. G. de, Ferreira, Tiago A. E.
Format: Article
Language:English
Subjects:
artificial neural networks
CHEMISTRY, MULTIDISCIPLINARY
particle swarm optimization
pollutants' concentration time series
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Summary:This study evaluates the application of an intelligent hybrid system for time-series forecasting of atmospheric pollutant concentration levels. The proposed method consists of an artificial neural network combined with a particle swarm optimization algorithm. The method not only searches relevant time lags for the correct characterization of the time series, but also determines the best neural network architecture. An experimental analysis is performed using four real time series and the results are shown in terms of six performance measures. The experimental results demonstrate that the proposed methodology achieves a fair prediction of the presented pollutant time series by using compact networks.
ISSN:0100-4042
1678-7064
DOI:10.1590/S0100-40422013000600007