Open Access
2013 Forecasting SO 2 Pollution Incidents by means of Elman Artificial Neural Networks and ARIMA Models
Antonio Bernardo Sánchez, Celestino Ordóñez, Fernando Sánchez Lasheras, Francisco Javier de Cos Juez, Javier Roca-Pardiñas
Abstr. Appl. Anal. 2013(SI10): 1-6 (2013). DOI: 10.1155/2013/238259


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.


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Antonio Bernardo Sánchez. Celestino Ordóñez. Fernando Sánchez Lasheras. Francisco Javier de Cos Juez. Javier Roca-Pardiñas. "Forecasting SO 2 Pollution Incidents by means of Elman Artificial Neural Networks and ARIMA Models." Abstr. Appl. Anal. 2013 (SI10) 1 - 6, 2013.


Published: 2013
First available in Project Euclid: 26 February 2014

zbMATH: 1314.91170
Digital Object Identifier: 10.1155/2013/238259

Rights: Copyright © 2013 Hindawi

Vol.2013 • No. SI10 • 2013
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