Machine learning regression and classification methods for fog events prediction

Research areas:

• Applications - Weather forecasting

Year:

2022

Type of Publication:

Article

Keywords:

Low-visibility events Orographic and hill-fogs Classification problems Regression problems Machine Learning algorithms

Authors:

• 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

Volume:

272

Pages:

106157

Month:

July

ISSN:

0169-8095

BibTex:

@article{FogCarlosCastillo, author = "C. Castillo-Bot{\'o}n and David Casillas-P{\'e}rez and Carlos Casanova-Mateo and S. Ghimire and E. Cerro-Prada and Pedro Antonio Guti{\'e}rrez and R.C. Deo and Sancho Salcedo-Sanz", 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.", awards = "JCR(2022): 5.5 Position: 18/94 (Q1) Category: METEOROLOGY {\&}amp; ATMOSPHERIC SCIENCES.", comments = "JCR(2022): 5.5 Position: 18/94 (Q1) Category: METEOROLOGY {\&}amp; ATMOSPHERIC SCIENCES.", doi = "10.1016/j.atmosres.2022.106157", issn = "0169-8095", journal = "Atmospheric Research", keywords = "Low-visibility events Orographic and hill-fogs Classification problems Regression problems Machine Learning algorithms", month = "July", note = "JCR(2022): 5.5 Position: 18/94 (Q1) Category: METEOROLOGY {\&}amp; ATMOSPHERIC SCIENCES.", pages = "106157", title = "{M}achine learning regression and classification methods for fog events prediction", url = "doi.org/10.1016/j.atmosres.2022.106157", volume = "272", year = "2022", }

Note:

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.

Comments:

JCR(2022): 5.5 Position: 18/94 (Q1) Category: METEOROLOGY & ATMOSPHERIC SCIENCES.