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Forecasting SO 2 Pollution Incidents by means of Elman Artificial Neural Networks and ARIMA Models

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  • Antonio Bernardo Sánchez
  • Celestino Ordóñez
  • Fernando Sánchez Lasheras
  • Francisco Javier de Cos Juez
  • Javier Roca-Pardiñas

Abstract

An SO 2 emission episode at coal-fired power station occurs when the series of bihourly average of SO 2 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 SO 2 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 SO 2 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.

Suggested Citation

  • Antonio Bernardo Sánchez & Celestino Ordóñez & Fernando Sánchez Lasheras & Francisco Javier de Cos Juez & Javier Roca-Pardiñas, 2013. "Forecasting SO 2 Pollution Incidents by means of Elman Artificial Neural Networks and ARIMA Models," Abstract and Applied Analysis, Hindawi, vol. 2013, pages 1-6, December.
    • Handle: RePEc:hin:jnlaaa:238259
    DOI: 10.1155/2013/238259
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    Cited by:

    1. Matyjaszek, Marta & Riesgo Fernández, Pedro & Krzemień, Alicja & Wodarski, Krzysztof & Fidalgo Valverde, Gregorio, 2019. "Forecasting coking coal prices by means of ARIMA models and neural networks, considering the transgenic time series theory," Resources Policy, Elsevier, vol. 61(C), pages 283-292.

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