Predicting Daily Average SO2 Concentrations in the Industrial Area of Syracuse (Italy)
In this paper artificial neural networks are used to build 1- day-ahead SO2 prediction models. The structure of the model was obtained following appropriate statistical analysis of the time series.
KeywordsWind Direction Multilayer Perceptron Neural Network International Data Exchange Related Time Series Prediction Model Structure
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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- Holland D. M., Principe P. P., Sickles J. E.: Trends in atmospheric sulfur and nitrogen species in the eastern United States for 1989-1995. Atmospheric Environment 33, pp. 37–49 (1999).Google Scholar
- Zannetti P.: Air Pollution Modeling, Theories, Computational Methods and A vailable Software Ed. Van Nostrand Reinhold, New York, 1990.Google Scholar
- Boznar M, Lesjak M., Mlakar P.: A Neural Network Based Method for Short-Terrn Predictions of Ambient SO2 Concentrations in Highly Polluted Industrial Areas of Complex Terrain. Atmospheric Environment, 27B, 2, pp. 221–230 (1993).Google Scholar
- Arena P., Baglio S., Castorina C., Fortuna L., Nunnari G.: A Neural Architecture to Predict Pollution in Industrial Areas. Proceedings of ICNN, 4, pp. 2107–2112, Washington (1996).Google Scholar
- Van Aalst R. M., De Leeuw F. A. A. M. (editors), National Ozone Forecasting System and International Data Exchange in Northwest Europe, European Topic Centre on Air Quality, 1997.Google Scholar
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