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Automated Aviation Wind Nowcasting: Exploring Feature-Based Machine Learning Methods
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Orientador(es)
Resumo(s)
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.
Descrição
Palavras-chave
Wind nowcasting Machine learning Feature selection Feature engineering Aviation wind nowcasting . Faculdade de Ciências Exatas e da Engenharia
Contexto Educativo
Citação
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
Editora
MDPI