Automated Aviation Wind Nowcasting: Exploring Feature-Based Machine Learning Methods
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.13/5561 |
Resumo: | Wind 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. |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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7160 |
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Automated Aviation Wind Nowcasting: Exploring Feature-Based Machine Learning MethodsWind nowcastingMachine learningFeature selectionFeature engineeringAviation wind nowcasting.Faculdade de Ciências Exatas e da EngenhariaWind 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.MDPIDigitUMaAlves, DécioMendonça, FábioMostafa, Sheikh ShanawazDias, Fernando Morgado2024-02-19T10:07:12Z20232023-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.13/5561engAlves, 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/ app13181022110.3390/app131810221info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-02-25T04:56:57Zoai:digituma.uma.pt:10400.13/5561Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:11:33.886019Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Automated Aviation Wind Nowcasting: Exploring Feature-Based Machine Learning Methods |
title |
Automated Aviation Wind Nowcasting: Exploring Feature-Based Machine Learning Methods |
spellingShingle |
Automated Aviation Wind Nowcasting: Exploring Feature-Based Machine Learning Methods Alves, Décio Wind nowcasting Machine learning Feature selection Feature engineering Aviation wind nowcasting . Faculdade de Ciências Exatas e da Engenharia |
title_short |
Automated Aviation Wind Nowcasting: Exploring Feature-Based Machine Learning Methods |
title_full |
Automated Aviation Wind Nowcasting: Exploring Feature-Based Machine Learning Methods |
title_fullStr |
Automated Aviation Wind Nowcasting: Exploring Feature-Based Machine Learning Methods |
title_full_unstemmed |
Automated Aviation Wind Nowcasting: Exploring Feature-Based Machine Learning Methods |
title_sort |
Automated Aviation Wind Nowcasting: Exploring Feature-Based Machine Learning Methods |
author |
Alves, Décio |
author_facet |
Alves, Décio Mendonça, Fábio Mostafa, Sheikh Shanawaz Dias, Fernando Morgado |
author_role |
author |
author2 |
Mendonça, Fábio Mostafa, Sheikh Shanawaz Dias, Fernando Morgado |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
DigitUMa |
dc.contributor.author.fl_str_mv |
Alves, Décio Mendonça, Fábio Mostafa, Sheikh Shanawaz Dias, Fernando Morgado |
dc.subject.por.fl_str_mv |
Wind nowcasting Machine learning Feature selection Feature engineering Aviation wind nowcasting . Faculdade de Ciências Exatas e da Engenharia |
topic |
Wind nowcasting Machine learning Feature selection Feature engineering Aviation wind nowcasting . Faculdade de Ciências Exatas e da Engenharia |
description |
Wind 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. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023 2023-01-01T00:00:00Z 2024-02-19T10:07:12Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.13/5561 |
url |
http://hdl.handle.net/10400.13/5561 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Alves, 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/ app131810221 10.3390/app131810221 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
MDPI |
publisher.none.fl_str_mv |
MDPI |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799137765751783424 |