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Abstract and Applied Analysis
Volume 2013, Article ID 238259, 6 pages
http://dx.doi.org/10.1155/2013/238259
Research Article

Forecasting SO2 Pollution Incidents by means of Elman Artificial Neural Networks and ARIMA Models

1Departamento de Construcción e Ingeniería de Fabricación, Universidad de Oviedo, 33203 Gijón, Spain
2Departamento de Explotación y Prospección de Minas, Universidad de Oviedo, Oviedo, 33004 Asturias, Spain
3Departamento de Estadística, Universidad de Vigo, 36310 Vigo, Spain

Received 29 October 2013; Accepted 13 November 2013

Academic Editor: L. Jódar

Copyright © 2013 Antonio Bernardo Sánchez et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

An SO2 emission episode at coal-fired power station occurs when the series of bihourly average of SO2 concentration, taken at 5-minute intervals, is greater than a specific value. Advance prediction of these episodes of pollution is very important for companies generating electricity by burning coal since it allows them to take appropriate preventive measures. In order to forecast SO2 pollution episodes, three different methods were tested: Elman neural networks, autoregressive integrated moving average (ARIMA) models, and a hybrid method combining both. The three methods were applied to a time series of SO2 concentrations registered in a control station in the vicinity of a coal-fired power station. The results obtained showed a better performance of the hybrid method over the Elman networks and the ARIMA models. The best prediction was obtained 115 minutes in advance by the hybrid model.