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 lowvisibility 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.

Automated summary

This paper provides a comprehensive analysis of low-visibility event prediction problems formulated as both regression and classification tasks. It discusses the performance of various ML approaches and evaluates their performance under a common comparison framework. The results can guide the selection of the most efficient formulation and best performing ML approaches for low-visibility event prediction.