Comparison of neural network models with ARIMA and regression models for prediction of Houston's daily maximum ozone concentrations

In an effort to forecast daily maximum ozone concentrations, many researchers have developed daily ozone forecasting models. However, this continuing worldwide environmental problem suggests the need for more accurate models. Development of these models is difficult because the meteorological variables and photochemical reactions involved in ozone formation are complex. In this study, a neural network model for forecasting daily maximum ozone levels is developed and compared with two conventional statistical models, regression and Box–Jenkins ARIMA. The results show that the neural network model is superior to the regression and Box–Jenkins ARIMA models we tested.