Forecasting daily total ozone concentration—a comparison between neurocomputing and statistical approaches

The present paper develops three predictive models for daily total ozone concentration over Arosa, Switzerland. The models are artificial neural network, multiple linear regression, and persistence forecast. Each model was judged for their predictive ability using analysis of variance, Pearson correlation study, and scatterplot analysis. Prediction errors were computed for each model. After painstaking analysis it was established that artificial neural network produces better forecasts than the statistical approaches like multiple linear regression and persistence forecast models.