Hourly forecasting of SO2 pollutant concentration using an elman neural network

  • Authors:

  • U. Brunelli;V. Piazza;L. Pignato;F. Sorbello;S. Vitabile

  • Affiliations:

  • Dipartimento di Ricerche Energetiche ed Ambientali, Università di Palermo, Palermo, Italy;Dipartimento di Ricerche Energetiche ed Ambientali, Università di Palermo, Palermo, Italy;Dipartimento di Ricerche Energetiche ed Ambientali, Università di Palermo, Palermo, Italy;Dipartimento Ingegneria Informatica, Università di Palermo, Palermo, Italy;Istituto di CAlcolo e Reti ad alte prestazioni, Italian National Research Council, Palermo, Italy

  • Venue:
  • WIRN'05 Proceedings of the 16th Italian conference on Neural Nets
  • Year:
  • 2005

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Abstract

In this paper the first results produced by an Elman neural network for hourly SO2 ground concentration forecasting are presented. Time series has been recorded between 1998 and 2001 and are referred to a monitoring station of SO2in the industrial site of Priolo, Syracuse, Italy. Data has been kindly provided by CIPA (Consorzio Industriale per la Protezione dell'Ambiente, Siracusa, Italia). Time series parameters are the horizontal and vertical wind velocity, the wind direction, the stability classes of Thomas, the base level of the layer of the atmospheric stability, the gradient of the potential temperature and the difference of the potential temperature of reference.