# Automated Aviation Wind Nowcasting: Exploring Feature-Based Machine Learning Methods
## 5 Discussion
The slight variations in metrics across different feature selection methods suggest that while some methods might yield minor improvements for certain metrics, they may not always ensure superior performance across all metrics and time intervals. In Figure 8, it is noteworthy that overall, nine features were selected more than 20 times (I2, I1, H2, A3, A2, A1, C2, L1, I4), and seven other features were selected more than 15 times (E2, L4, J2, E4, E3, L2, L3). The most selected feature, with 24 occurrences, was I2, while the least selected, with four incidences, was H4. The control set of features contained 12 features for each target and nowcasted time, while the group with all featurescontained 56. The mRMR and XGBoost methods each selected ten features for every target and projected time. When considering the two-minute nowcast, the GA method's feature selection varied depending on the target: 26 for H2, 27 for I2, and 28 for C2. In the case of the ten-minute nowcast, the feature selections for each respective target were 30, 26, and 28, respectively. Lastly, the chosen features numbered 32, 26, and 35, respectively, for the most extended nowcast period. In a similar pattern, RFECV opted for 12, 14, and 12 features, respectively, for the first set, and 11, 14, and 18, respectively, for the second set. For the longest prediction interval, it utilized nearly all the features, specifically 55, 55, and 56. For PCA, the number of features was 20, 20, and 13, respectively, for the 2 min forecast, while it was 14, 22, and 21, respectively, for the 10 min and 21, 20, and 19 for the 20 min prediction, for H2, I2, and C2 target, respectively. It is also renowned that, in general, as the forecasted time increases (from 2 to 20 min), the number of selected features also increases, suggesting that the additional information of the less relevant features becomes progressively more relevant to provide context. The salient outcome of this study highlights a pronounced spatial correlation among sensors located at variable distances from the intended prediction point, which corresponded to the middle of the runway, at the position of the MID wind sensor. Figure 7 extrapolates this correlation, providing computational details between each feature, target, and prediction timeframe, particularly underscoring the strong correlation of features from sensors such as C1, E3, H3, and E1, which rank within the top 10. An illustration of these findings can also be seen in Figure 6, where the individual sensor features demonstrate distinct intra-sensor distributions but exhibit similarity when compared to features across other sensors, emphasizing a robust sensor intercorrelation. This correlation is further supported by Figure 8, indicating the selection of features from diverse sensors across all prediction targets and windows. In conjunction with the observed fluctuations in feature importance across different nowcast durations, this correlation underscores the significance of the spatial-temporal context in enhancing prediction precision. This understanding augments our capacity to discern intricate spatial patterns in wind behavior, thereby harboring the potential to optimize wind nowcast models over various time spans. Table 5 provides a comprehensive comparison of the wind speed forecasting methodology against existing state-of-the-art approaches. Notably, only the study by [PERSON] et al. (2017) spanned a longer duration than this study. However, the current work boasts a time resolution that is five times finer, and its performance metrics remain comparable, underscoring the significance and robustness of the methodology. Many of the referenced studies utilized considerably shorter timeframes, potentially limiting their ability to discern synoptic wind patterns effectively. In terms of performance, the results from the current research align well with the prevailing state of the art, even with the enhanced resolution and higher data volume.