Comparative assessment of neural networks and regression models for forecasting summertime ozone in Athens

A comparison study has been performed with neural networks (NNs) and multiple linear regression models to forecast the next dayʹs maximum hourly ozone concentration in the Athens basin at four representative monitoring stations that show very different behavior. All models use 11 predictors (eight meteorological and three persistence variables) and are developed and validated between April and October from 1992 to 1999. Performance results based on a wide set of forecast quality measures indicate that the NNs provide better estimates of ozone concentrations at the monitoring sites, whilst the more often used linear models are less efficient at accurately forecasting high ozone concentrations. The violation of the European information threshold of 180 μg/m3 is successfully predicted by the NN in 72% of the cases on average. Results at all stations are consistent with similar ozone forecast studies using NNs in other European cities.