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A combined hard and soft computing approach for ground-level ozone monitoring

The paper deals with the development of a public warning system for ground-level ozone monitoring in Bordeaux, France. Meteorological variables are used as input in order to obtain an estimate of the next day's maximum ozone concentration as well as a measure of its temporal extent. The proposed approach combines adaptive nonlinear state-space modelling techniques, a gain scheduling strategy and multi-layer perceptron neural networks.