A NEURAL-NETWORK MODEL FORECASTING FOR PREDICTION OF DAILY MAXIMUM OZONE CONCENTRATION IN AN INDUSTRIALIZED URBAN AREA

Prediction of ambient ozone concentrations in urban areas would allow evaluation of such factors as compliance and noncompliance with EPA re quirements. Though ozone prediction models exist, there is still a nee d for more accurate models. Development of these models is difficult b ecause the meteorological variables and photo-chemical reactions invol ved in ozone formation are complex. In this study, we developed a neur al network model for forecasting daily maximum ozone levels. We then c ompared the neural network's performance with those of two traditional statistical models, regression, and Box-Jenkins ARIMA. The neural net work model for forecasting daily maximum ozone levels is different fro m the two statistical models because it employs a pattern recognition approach. Such an approach does not require specification of the struc tural form of the model. The results show that the neural network mode l is superior to the regression and Box-Jenkins ARIMA models we tested . Copyright (C) 1996 Elsevier Science Ltd