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Volume 16
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10.3390/atmos16091073
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Article

Artificial Intelligence-Based Methods and Algorithms in Fog and Atmospheric Low-Visibility Forecasting

by
Sancho Salcedo-Sanz
1,*,
David Guijo-Rubio
2,
Jorge Pérez-Aracil
1,
César Peláez-Rodríguez
1,
Antonio Manuel Gomez-Orellana
2 and
Pedro Antonio Gutiérrez-Peña
2
1
Department of Signal Processing and Communications, Universidad de Alcalá, 28805 Alcalá de Henares, Spain
2
Department of Computer Science and Artificial Intelligence, Universidad de Córdoba, 14014 Cordoba, Spain
*
Author to whom correspondence should be addressed.
Atmosphere 2025, 16(9), 1073; https://doi.org/10.3390/atmos16091073
Submission received: 7 July 2025 / Revised: 1 September 2025 / Accepted: 7 September 2025 / Published: 11 September 2025
(This article belongs to the Special Issue Numerical Simulation and Forecast of Fog)
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Abstract

The accurate prediction of atmospheric low-visibility events due to fog, haze or atmospheric pollution is an extremely important problem, with major consequences for transportation systems, and with alternative applications in agriculture, forest ecology and ecosystems management. In this paper, we provide a comprehensive literature review and analysis of AI-based methods applied to fog and low-visibility events forecasting. We also discuss the main general issues which arise when dealing with AI-based techniques in this kind of problem, open research questions, novel AI approaches and data sources which can be exploited. Finally, the most important new AI-based methodologies which can improve atmospheric visibility forecasting are also revised, including computational experiments on the application of ordinal classification approaches to a problem of low-visibility events prediction in two Spanish airports from METAR data.
Keywords: fog episodes; low-visibility events; artificial intelligence; machine learning; ordinal classification; ordinal regression; XAI fog episodes; low-visibility events; artificial intelligence; machine learning; ordinal classification; ordinal regression; XAI

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MDPI and ACS Style

Salcedo-Sanz, S.; Guijo-Rubio, D.; Pérez-Aracil, J.; Peláez-Rodríguez, C.; Gomez-Orellana, A.M.; Gutiérrez-Peña, P.A. Artificial Intelligence-Based Methods and Algorithms in Fog and Atmospheric Low-Visibility Forecasting. Atmosphere 2025, 16, 1073. https://doi.org/10.3390/atmos16091073

AMA Style

Salcedo-Sanz S, Guijo-Rubio D, Pérez-Aracil J, Peláez-Rodríguez C, Gomez-Orellana AM, Gutiérrez-Peña PA. Artificial Intelligence-Based Methods and Algorithms in Fog and Atmospheric Low-Visibility Forecasting. Atmosphere. 2025; 16(9):1073. https://doi.org/10.3390/atmos16091073

Chicago/Turabian Style

Salcedo-Sanz, Sancho, David Guijo-Rubio, Jorge Pérez-Aracil, César Peláez-Rodríguez, Antonio Manuel Gomez-Orellana, and Pedro Antonio Gutiérrez-Peña. 2025. "Artificial Intelligence-Based Methods and Algorithms in Fog and Atmospheric Low-Visibility Forecasting" Atmosphere 16, no. 9: 1073. https://doi.org/10.3390/atmos16091073

APA Style

Salcedo-Sanz, S., Guijo-Rubio, D., Pérez-Aracil, J., Peláez-Rodríguez, C., Gomez-Orellana, A. M., & Gutiérrez-Peña, P. A. (2025). Artificial Intelligence-Based Methods and Algorithms in Fog and Atmospheric Low-Visibility Forecasting. Atmosphere, 16(9), 1073. https://doi.org/10.3390/atmos16091073

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