Prediction of Ozone Formation Based on Neural Network

The atmospheric ozone concentration in Seoul was forecasted using an artificial neural network and spatiotemporal analysis. The artificial neural network 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 h and 16-21 h. The resulting predictions of ozone formation were compared to measured data. From the comparison, it was found that the neural network method gave reliable accuracy within a limited prediction horizon.