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Intelligent visibility forecasting at airports: a systematic review
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Orientador(es)
Resumo(s)
<jats:title>Abstract</jats:title>
<jats:p>Low visibility conditions caused by phenomena such as fog, heavy rain, or snowfall impose major operational and safety challenges at airports. Conventional numerical weather prediction models, although improved over time, still struggle to forecast low visibility accurately due to scale mismatches and uncertainties in physical parameterizations, among other factors. This review synthesizes findings on data-driven solutions, including machine learning and deep learning, that harness large datasets to reveal hidden patterns, offering better performance and adaptability. Ensemble methods and the integration of multiple data sources further enhance accuracy, particularly for short lead times. Several methods achieve correlation coefficients above 0.90 and root mean square error below 1 km, yet generalization and integration into real-time airport operations remain underexplored. Future work should focus on transferability across diverse climates, integration with advanced operational tools, and bridging gaps between model complexity and user interpretability. The next generation of hybrid forecasting frameworks has the potential to enhance safety, limit economic losses, and improve resilience in airport operations.</jats:p>
Descrição
Palavras-chave
Visibility forecasting Machine learning Airport operations Fog prediction Artificial intelligence . Faculdade de Ciências Exatas e da Engenharia
Contexto Educativo
Citação
Alves, D., Belo-Pereira, M., Mendonça, F., & Morgado-Dias, F. (2025). Intelligent visibility forecasting at airports: A systematic review. Environmental Research Communications, 7(6), 062002.
Editora
IOP Publishing