[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125921-en":3,"doc-seo-125921-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},125921,2336474459895,"Aria","https://ap-avatar.wpscdn.com/avatar/22000baeef7a5ed0655?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786071322749376916",8,"Research & Report","Automated Aviation Wind Nowcasting - Exploring Feature-Based Machine Learning Methods","Wind factors strongly affect air travel, and extreme conditions can disrupt airport operations. Machine learning is increasingly used to forecast wind patterns using meaningful predictors. This research uses Madeira International Airport to evaluate how feature engineering and feature selection improve wind nowcasting, targeting wind speed, direction, and gusts. Data from four sensors generated 56 features for 2, 10, and 20 minute intervals, and five selection methods were compared to improve accuracy and efficiency.","applied sciences  \nArticle  \nAutomated Aviation Wind Nowcasting: Exploring Feature-Based Machine Learning Methods  \nD²cio Alves 1,2, *, F¡bio Mendonça 1,2, Sheikh Shanawaz Mostafa 2 and Fernando Morgado-Dias 1,2  \nCitation: Alves, D.; Mendonça, F.; Mostafa, S.S.; Morgado-Dias, F. Automated Aviation Wind Nowcasting: Exploring  \nFeature-Based Machine Learning Methods. Appl. Sci. 2023, 13, 10221 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)app131810221  \nAcademic Editors: Tsung-Jung Liu and Kuan-Hsien Liu  \nReceived: 15 August 2023  \nRevised: 7 September 2023  \nAccepted: 10 September 2023  \nPublished: 12 September 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Faculty of Exact Sciences and Engineering, University of Madeira, 9020-105 Funchal, Portugal; [fabioruben@staff.uma.pt](fabioruben@staff.uma.pt) (F.M.); [morgado@staff.uma.pt](morgado@staff.uma.pt) (F.M.-D.)  \n2 Interactive Technologies Institute (ITI/LARSyS and ARDITI), 9020-105 Funchal, Portugal; [sheikh.mostafa@tecnico.ulisboa.pt](sheikh.mostafa@tecnico.ulisboa.pt)  \n* Correspondence: [decio.alves@iti.tecnico.ulisboa.pt](decio.alves@iti.tecnico.ulisboa.pt)  \nAbstract: 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 patterns. 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 featureselection 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.  \nKeywords: wind nowcasting; machine learning; feature selection; feature engineering; aviation wind nowcasting  \n1. Introduction  \nAirflow, including wind speed and direction, is a crucial element that significantlyinfluences aeronautical operations [1] . Extreme wind conditions can disrupt airport and air traffic operations, highlighting the necessity for precise measurements and predictions of wind near the takeoff and landing zones [2] . Machine learning (ML), with its ability to model complex non-linear relationships and adapt to new data, has emerged as a promising approach for wind prediction, achieving good performances and the ability to operate at acceptable timescales [3–5] .  \nIn this context, the significance of the input data's quality and relevance to the performance of models has been emphasized extensively in scholarly discussions. The literature reiterates a fundamental tenet that the quality of the training data dictates the performance ceiling of a given ML model. A surge of relentless efforts, including the refinement of methods for feature selection, is currently being channeled towards optimizing both the quality and performance of these models [6,7] .  \nFeature selection, a process that identifies and selects the most pertinent features from a dataset,","cbCaittEc73OAuYD","https://ap.wps.com/l/cbCaittEc73OAuYD","pdf",4953199,4,1,23,"English","en",105,"# Introduction\n## Feature Selection and Motivation\n## Research Objective and Scope\n# Methodology and Feature Engineering\n## Data, Features, and Time Intervals\n## Feature Selection Techniques\n# Results and Discussion\n## Prediction Accuracy Across Intervals\n## Spatial Correlation and Challenges\n# Conclusions","[{\"question\":\"What forecasting targets are evaluated in the study?\",\"answer\":\"The study predicts wind speed, wind direction, and wind gusts using sensor data and machine learning models.\"},{\"question\":\"How many features and sensors are used for the nowcasting experiments?\",\"answer\":\"Four sensors provide data to create 56 features used for forecasting across 2, 10, and 20 minute intervals.\"},{\"question\":\"Which feature selection techniques are compared?\",\"answer\":\"Five techniques are analyzed: mRMR, PCA, RFECV, GA, and XGBoost, to determine their impact on accuracy and efficiency.\"}]","Automated Aviation Wind Nowcasting - 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