# Automated Aviation Wind Nowcasting: Exploring Feature-Based Machine Learning Methods
###### Abstract  
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 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.  
wind nowcasting; machine learning; feature selection; feature engineering; aviation wind nowcasting +
Footnote †: journal: Applied Sciences  
0000-0002-2000-1000-0000-0000-0000-0000-0000-0000-0000-0000-00000-0000-00000-000  
The significance of feature selection is multifaceted, reducing computational costs, identifying irrelevant features, and aiding the whole generation of well-classified models [8]. Focusing on the most relevant features also eliminates noise and redundancy, thereby improving the performance and accuracy while also reducing the dimensionality and optimizing the overall ML approaches in diverse fields [9; 10].  
With the maturation of technology and the concurrent advancement in ML methodologies, a trend toward more intricate and profound models becomes evident. Concomitant with these developments is the emergence of new paradigms in feature selection, as researchers are now endeavoring to incorporate feature selection techniques into deep learning models, underscoring the burgeoning significance of this approach even within the context of contemporary, high-dimensionality, and complex models [8; 9; 10; 11].  
The current research explores various feature selection methodologies using wind data from Madeira Airport as a case study and aims to compare the effectiveness of different techniques through a comparative analysis of each set of features. Specifically, it is intended to assess the effectiveness of the created features for predicting the following wind-related metrics. The prime objective is to deepen the comprehension of feature creation and selection strategies for wind prediction and contribute to refining meteorological nowcasts using ML models.  
The principal innovations of this work are as follows:  
* A comprehensive analysis focused on exploration of the effective features within the understudied domain of ML-based wind nowcasting.
* A first application of feature engineering to wind speed, direction, and gust, employing five distinct techniques.
* A first exploration into the significance of feature importance, evaluated over multiple prediction steps for wind nowcasting.  
This study is divided into six sections. Section 2 provides an overview of the current state of the art in feature selection for wind prediction using ML. Section 3 describes the materials and methods employed in this study, while Section 4 presents the results. Section 5 conducts an analysis and discussion of the results, which is further concluded in Section 6.