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Intelligent visibility forecasting at airports: a systematic review

2025-06-01Artigo científico Acesso aberto
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datacite.subject.fosCiências Naturais::Ciências da Computação e da Informação
datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
datacite.subject.fosEngenharia e Tecnologia::Engenharia Civil
dc.contributor.authorAlves, Décio
dc.contributor.authorBelo-Pereira, Margarida
dc.contributor.authorMendonça, Fábio
dc.contributor.authorMorgado-Dias, Fernando
dc.contributor.authorAlves, Decio
dc.contributor.authorSilva Mendonça, Fábio Rúben
dc.contributor.authorMorgado-Dias, Fernando
dc.date.accessioned2026-08-07T10:36:19Z
dc.date.available2026-08-07T10:36:19Z
dc.date.issued2025-06-01
dc.description.abstract<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>eng
dc.identifier.citationAlves, D., Belo-Pereira, M., Mendonça, F., & Morgado-Dias, F. (2025). Intelligent visibility forecasting at airports: A systematic review. Environmental Research Communications, 7(6), 062002.
dc.identifier.doi10.1088/2515-7620/addea9
dc.identifier.issn2515-7620
dc.identifier.urihttp://hdl.handle.net/10400.13/7914
dc.language.isoeng
dc.peerreviewedyes
dc.publisherIOP Publishing
dc.relationLARSyS - Laboratory of Robotics and Engineering Systems
dc.relationLaboratory of Robotics and Engineering Systems
dc.relationLaboratory of Robotics and Engineering Systems
dc.relation.ispartofEnvironmental Research Communications
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectVisibility forecasting
dc.subjectMachine learning
dc.subjectAirport operations
dc.subjectFog prediction
dc.subjectArtificial intelligence
dc.subject.
dc.subjectFaculdade de Ciências Exatas e da Engenharia
dc.titleIntelligent visibility forecasting at airports: a systematic revieweng
dc.typejournal article
dspace.entity.typePublication
oaire.awardNumberLA/P/0083/2020
oaire.awardNumberUIDP/50009/2020
oaire.awardNumberUIDB/50009/2020
oaire.awardTitleLARSyS - Laboratory of Robotics and Engineering Systems
oaire.awardTitleLaboratory of Robotics and Engineering Systems
oaire.awardTitleLaboratory of Robotics and Engineering Systems
oaire.awardURIhttp://hdl.handle.net/10400.13/7898
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F50009%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50009%2F2020/PT
oaire.citation.issue6
oaire.citation.titleEnvironmental Research Communications
oaire.citation.volume7
oaire.fundingStreamConcurso para Atribuição do Estatuto e Financiamento de Laboratórios Associados (LA)
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameAlves
person.familyNameSilva Mendonça
person.familyNameMorgado-Dias
person.givenNameDecio
person.givenNameFábio Rúben
person.givenNameFernando
person.identifier.ciencia-id7F1E-8AE9-3098
person.identifier.ciencia-id7B14-DF07-AA6D
person.identifier.orcid0009-0001-2972-6505
person.identifier.orcid0000-0002-5107-3248
person.identifier.orcid0000-0001-7334-3993
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
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