Paper Title:
The Prediction of SO2 Pollutant Concentration Using a RBF Neural Network
  Abstract

Air pollution may cause pernicious effects on human health, and is a widespread problem in the world. Air quality management systems have became an important research issue with strong implications for inhabitants’ health. Monitoring and forecasting of air quality indicators plays an important role in the management systems. Artificial intelligent techniques are successfully used in modelling of highly complex and nonlinear phenomena. In this paper, a model, which is radial basis function (RBF) neural network, is established to estimate the impact of meteorological indicators on SO2. The proposed model achieves 9.91% in mean absolute percentage error (MAPE) compared to real observation data sequence. For air quality, it could be a promising candidate for forecasting the air quality indicators data sequence.

  Info
Periodical
Edited by
Qi Luo
Pages
1392-1396
DOI
10.4028/www.scientific.net/AMM.55-57.1392
Citation
C. Y. Wang, W. Y. Zhang, J. J. Wang, W. F. Zhao, "The Prediction of SO2 Pollutant Concentration Using a RBF Neural Network", Applied Mechanics and Materials, Vols. 55-57, pp. 1392-1396, 2011
Online since
May 2011
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