Statistical prediction of air pollution levels using non-physical models

This paper treats the prediction problem of air pollution levels at a short range by non-physical models. Main results are given as follows: (i) The prediction accuracy of the pollution levels by time series models is compared by evaluating three performance indices, and it is shown that the multiple linear regression model already proposed is better than the auto-regressive model, the Box-Jenkins' model and the persistence model. (ii) The multiple linear regression model is more improved if the model is classified by weather. (iii) The modeling accuracy is discussed for various sample sizes, and an appropriate sample size is determined from the experiment. (iv) The confidence intervals of the predicted means at a fixed time are calculated, and the combinations of the measurement times and the measured factors that improve the prediction accuracy are chosen. (v) A revised GMDH is proposed and the accuracy by this method is more improved than those by the time series models already presented. (vi) The Kalman filtering method is applied to the prediction of pollution levels, and the measured factors that improve the prediction accuracy are chosen.