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:
-
- Castillo-Botón, C.
- Casillas-Pérez, David
- Casanova-Mateo, Carlos
- Ghimire, S.
- Cerro-Prada, E.
- Gutiérrez, Pedro Antonio
- Deo, R. C.
- Salcedo-Sanz, Sancho
- 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.