PREDICTION OF OZONE FORMATION BASED ON NEURAL NETWORK

The atmospheric ozone concentration in Seoul, Korea, was forecasted using an artificial neural network (ANN) and spatiotemporal analysis. The ANN was trained by using hourly pollutant and meteorological data that resulted in complex patterns of ozone formation. The finite-volume method was employed in the spatiotemporal analysis in order to take into account the effects of wind. Time horizons in the forecasts were 1-6 hours and 16-21 hours. The resulting predictions of ozone formation were compared with measured data. From the comparison, it was found that the ANN method gave reliable accuracy within a limited prediction.

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  • English

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  • Accession Number: 00795823
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Aug 1 2000 12:00AM