Machine learning regression and classification methods for fog events prediction

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Áreas de investigación:
Año:
2022
Tipo de publicación:
Artículo
Palabras clave:
Low-visibility events Orographic and hill-fogs Classification problems Regression problems Machine Learning algorithms
Autores:
Journal:
Atmospheric Research
Volumen:
272
Páginas:
106157
Mes:
July
ISSN:
0169-8095
BibTex:
Nota:
JCR(2022): 5.5 Position: 18/94 (Q1) Category: METEOROLOGY & ATMOSPHERIC SCIENCES.
Abstract:
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.
Comentarios:
JCR(2022): 5.5 Position: 18/94 (Q1) Category: METEOROLOGY & ATMOSPHERIC SCIENCES.
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