Machine learning regression and classification methods for fog events prediction.

Atmospheric low-visibility events are usually associated with fog formation. Extreme low-visibility events deeply affect the air and ground transportation, airports and motor-road facilities causing accidents and traffic problems every year. Machine Learning (ML) algorithms have been successfully applied to many fog formation and low-visibility prediction problems. The associated problem can be formulated either as a regression or as a classification task, which has an impact on the type of ML approach to be used and on the quality of the predictions obtained. In this paper we carry out a complete analysis of low-visibility events prediction problems, formulated as both regression and classification problems. We discuss the performance of a large number of ML approaches in each type of problem, and evaluate their performance under a common comparison framework. According to the obtained results, we will provide indications on what the most efficient formulation is to tackle low-visibility predictions and the best performing ML approaches for low-visibility events prediction. [Display omitted] • We deal with the prediction of low-visibility events through Machine Learning algorithms. • We formulate the problem as regression and different classification tasks. • The specific characteristics of each formulation type are described and analyzed. • Machine Learning approaches are compared and sorted by performance in a specific problem. [ABSTRACT FROM AUTHOR]