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Automated Aviation Wind Nowcasting: Exploring Feature-Based Machine Learning Methods

2023Journal article Open access
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dc.contributor.authorAlves, Décio
dc.contributor.authorMendonça, Fábio
dc.contributor.authorMostafa, Sheikh Shanawaz
dc.contributor.authorDias, Fernando Morgado
dc.date.accessioned2024-02-19T10:07:12Z
dc.date.available2024-02-19T10:07:12Z
dc.date.issued2023
dc.description.abstractWind factors significantly influence air travel, and extreme conditions can cause operational disruptions. Machine learning approaches are emerging as a valuable tool for predicting wind pat terns. This research, using Madeira International Airport as a case study, delves into the effectiveness of feature creation and selection for wind nowcasting, focusing on predicting wind speed, direction, and gusts. Data from four sensors provided 56 features to forecast wind conditions over intervals of 2, 10, and 20 min. Five feature selection techniques were analyzed, namely mRMR, PCA, RFECV, GA, and XGBoost. The results indicate that combining new wind features with optimized feature selection can boost prediction accuracy and computational efficiency. A strong spatial correlation was observed among sensors at different locations, suggesting that the spatial-temporal context enhances predictions. The best accuracy for wind speed forecasts yielded a mean absolute percentage error of 0.35%, 0.53%, and 0.63% for the three time intervals, respectively. Wind gust errors were 0.24%, 0.33%, and 0.38%, respectively, while wind direction predictions remained challenging with errors above 100% for all intervals.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationAlves, D.; Mendonça, F.; Mostafa, S.S.; Morgado-Dias, F. Automated Aviation Wind Nowcasting: Exploring Feature-Based Machine Learning Methods. Appl. Sci. 2023, 13, 10221. https://doi.org/10.3390/ app131810221pt_PT
dc.identifier.doi10.3390/app131810221pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.13/5561
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relationLaboratory of Robotics and Engineering Systems
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectWind nowcastingpt_PT
dc.subjectMachine learningpt_PT
dc.subjectFeature selectionpt_PT
dc.subjectFeature engineeringpt_PT
dc.subjectAviation wind nowcastingpt_PT
dc.subject.pt_PT
dc.subjectFaculdade de Ciências Exatas e da Engenhariapt_PT
dc.titleAutomated Aviation Wind Nowcasting: Exploring Feature-Based Machine Learning Methodspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleLaboratory of Robotics and Engineering Systems
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50009%2F2020/PT
oaire.citation.issue18pt_PT
oaire.citation.startPage10221pt_PT
oaire.citation.titleApplied Sciencespt_PT
oaire.citation.volume13pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameAlves
person.familyNameSilva Mendonça
person.familyNameMostafa
person.familyNameMorgado-Dias
person.givenNameDecio
person.givenNameFábio Rúben
person.givenNameSheikh Shanawaz
person.givenNameFernando
person.identifier34497
person.identifier.ciencia-id7F1E-8AE9-3098
person.identifier.ciencia-idEE14-BEB3-F82B
person.identifier.ciencia-id7B14-DF07-AA6D
person.identifier.orcid0009-0001-2972-6505
person.identifier.orcid0000-0002-5107-3248
person.identifier.orcid0000-0002-7677-0971
person.identifier.orcid0000-0001-7334-3993
person.identifier.ridN-9228-2015
person.identifier.scopus-author-id55489640900
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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