Machine learning regression and classification methods for fog events prediction
Article
Article Title | Machine learning regression and classification methods for fog events prediction |
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ERA Journal ID | 1956 |
Article Category | Article |
Authors | Castillo-Boton, C. (Author), Casillas-Perez, D. (Author), Casanova-Mateo, C. (Author), Ghimire, S. (Author), Cerro-Prada, E. (Author), Gutierrez, P. A. (Author), Deo, R. C. (Author) and Salcedo-sanz, S. |
Journal Title | Atmospheric Research |
Journal Citation | 272, pp. 1-23 |
Article Number | 106157 |
Number of Pages | 23 |
Year | 2022 |
Publisher | Elsevier |
Place of Publication | Netherlands |
ISSN | 0169-8095 |
1873-2895 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.atmosres.2022.106157 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0169809522001430 |
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. |
Keywords | Low-visibility events; orographic and hill-fogs; Classification problems; Regression problems; Machine Learning algorithms |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 460207. Modelling and simulation |
370104. Atmospheric composition, chemistry and processes | |
Institution of Origin | University of Southern Queensland |
Byline Affiliations | University of Alcala, Spain |
Rey Juan Carlos University, Spain | |
Polytechnic University of Madrid, Spain | |
School of Mathematics, Physics and Computing | |
University of Cordoba, Spain | |
School of Sciences | |
Funding source | Grant ID Spanish Ministry of Science and Innovation (MICINN), through Project Number PID2020- 115454GB-C21 |
https://research.usq.edu.au/item/q7591/machine-learning-regression-and-classification-methods-for-fog-events-prediction
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Carlos_Fog_Regression_Classification-Published.pdf | ||
License: CC BY-NC-ND 4.0 | ||
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