Abstract
The paper investigates the application of black box modelling to the prediction of the daily maxima of ground-ozone level. The main interest of these modelling approaches is their genericity as they are solely based on the available data provided by the Associations of air quality monitoring and they can be transposed from a geographical area to another one. The paper realises a comparative study of four statistical learning approaches, the decisions trees (, the neural networks, the least-angle regression and the support vector regression, to the ozone level prediction. Before concluding, the obtained results and their comments are presented.
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