Machine learning regression and classification methods for fog events prediction

Castillo-Boton, C. and Casillas-Perez, D. and Casanova-Mateo, C. and Ghimire, S. and Cerro-Prada, E. and Gutierrez, P. A. and Deo, R. C. ORCID: https://orcid.org/0000-0002-2290-6749 and Salcedo-Sanz, S. (2022) Machine learning regression and classification methods for fog events prediction. Atmospheric Research, 272:106157. pp. 1-23. ISSN 0169-8095

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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.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: Current – Faculty of Health, Engineering and Sciences - School of Mathematics, Physics and Computing (1 Jan 2022 -)
Faculty/School / Institute/Centre: Current – Faculty of Health, Engineering and Sciences - School of Mathematics, Physics and Computing (1 Jan 2022 -)
Date Deposited: 26 May 2022 03:02
Last Modified: 26 May 2022 03:02
Uncontrolled Keywords: Low-visibility events; orographic and hill-fogs; Classification problems; Regression problems; Machine Learning algorithms
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460207 Modelling and simulation
37 EARTH SCIENCES > 3701 Atmospheric sciences > 370104 Atmospheric composition, chemistry and processes
Socio-Economic Objectives (2020): 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280115 Expanding knowledge in the information and computing sciences
Funding Details:
Identification Number or DOI: https://doi.org/10.1016/j.atmosres.2022.106157
URI: http://eprints.usq.edu.au/id/eprint/48575

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