Machine learning regression and classification methods for fog events prediction

dc.contributor.authorCastillo-Botón, Carlos
dc.contributor.authorCasillas-Pérez, David
dc.contributor.authorCasanova-Mateo, Carlos
dc.contributor.authorGhimire, Sujan
dc.contributor.authorCerro-Prada, Elena
dc.contributor.authorGutierrez, P.A.
dc.contributor.authorDeo, Ravinesh
dc.contributor.authorSalcedo-Sanz, Sancho
dc.date.accessioned2023-09-21T07:55:12Z
dc.date.available2023-09-21T07:55:12Z
dc.date.issued2022
dc.descriptionThis research has been partially supported by Spanish Ministry of Science and Innovation (MICINN), through Project Number PID2020-115454GB-C21.es
dc.description.abstractAtmospheric 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.es
dc.identifier.citationC. Castillo-Botón, D. Casillas-Pérez, C. Casanova-Mateo, S. Ghimire, E. Cerro-Prada, P.A. Gutierrez, R.C. Deo, S. Salcedo-Sanz, Machine learning regression and classification methods for fog events prediction, Atmospheric Research, Volume 272, 2022, 106157, ISSN 0169-8095, https://doi.org/10.1016/j.atmosres.2022.106157es
dc.identifier.doi10.1016/j.atmosres.2022.106157es
dc.identifier.issn1873-2895
dc.identifier.urihttps://hdl.handle.net/10115/24430
dc.language.isoenges
dc.publisherElsevieres
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectLow-visibility eventses
dc.subjectOrographic and hill-fogses
dc.subjectClassification problemses
dc.subjectRegression problemses
dc.subjectMachine Learning algorithmses
dc.titleMachine learning regression and classification methods for fog events predictiones
dc.typeinfo:eu-repo/semantics/articlees

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