# Automated Aviation Wind Nowcasting: Exploring Feature-Based Machine Learning Methods
## 6 Conclusions
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Work** & **Time Resolution** & **FS Methods** & **Data** & **Performance** \\ \hline [PERSON] et al. [12] & 10 min & CRO & 1 year & MSE 2.50 \\ [PERSON] et al. [13] & More than 1 h & PCA-based & 1 month & MAE 0.20, MAPE 0.05, and RMSE 0.45 \\ [PERSON] and [PERSON] [14] & 15 min & Relief & 1 month & MSE 0.48 \\ [PERSON] et al. [15] & 1 h & Emprical mode & 1 month & MAE 0.60, MAPE 13.30, and RMSE 0.79 \\ [PERSON] et al. [16] & 1 h & Deep feature selection & 1 year & MAE 3.76 and NRMSE 5.21 \\ [PERSON] et al. [17] & 10 min & Mutual information based & 9 months & MAE 0.31, MAPE 7.13, and RMSE 0.39 \\ [PERSON] et al. [18] & 10 min & Hybrid feature selection & 3 years & MAE 0.47 \\ [PERSON] et al. [19] & 15 min & XGBoost based & 6 months & \\ [PERSON] et al. [20] & 1 h & ADGWDTO & - & MAE 0.002 and RMSE 0.003 \\ [PERSON] and [PERSON] [21] & 1 h & FWNSDEC-SSA & 1 year & MAE 0.04, MAPE 1.09, and RMSE 0.07 \\ This work & 2 min & mRMR, PCA, RFCV, GA & 2 years & MAE 1.00, MAPE 0.35, and MSE 1.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: State-of-the-art analysis for feature selection for wind speed forecast. expenses with marginal benefits. A closer examination of the results clearly indicates that the performance of the control dataset was often as effective, if not more so, than the datasets with engineered features. Regarding feature selection, while it is effective in reducing the feature count and easing the model training process, it might not be essential for wind speed and direction. This is mainly because creating additional features for these parameters might be impractical. Future research should delve more deeply into wind direction nowcasting, with a focus on predicting wind components and incorporating observed spatial correlations among sensors into prediction models. Furthermore, additional studies are warranted to evaluate the benefits of more complex and deeper machine learning models, considering the trade-off between the computational cost and improved performance. Such efforts could significantly enhance wind nowcasting, benefiting all sectors reliant on wind nowcast data. The principal limitations of this study reside in its geographic scope, the temporal extent of the data assembled, and the inherent challenges of utilizing regression trees. The study's confinement to a specific geographic region might limit the universality of its findings, potentially affecting their applicability to diverse locations. Regression trees can be sensitive to minor data variations, leading to potential inconsistencies in predictions, and can easily overfit to the training data if not adequately pruned, which can compromise their generalizability to new datasets. Furthermore, while the two-year data timeframe provides valuable insights, it might restrict the breadth of the study's conclusions, especially when identifying trends and patterns over longer time periods. Conceptualization, D.A. and F.M. ; methodology, D.A. and F.M. ; software, D.A. ; validation, D.A., F.M. and S.S.M. ; formal analysis, D.A., F.M., S.S.M. and F.M.-D.; investigation, F.M.-D.; resources, D.A. ; data curation, D.A. ; writing--original draft preparation, D.A. and F.M. ; writing--review and editing, D.A., F.M., S.S.M. and F.M.-D.; visualization, D.A., F.M., S.S.M. and F.M.-D.; supervision, F.M., S.S.M. and F.M.-D.; project administration, D.A. ; funding acquisition, D.A. All authors have read and agreed to the published version of the manuscript. This research was funded by LARSyS (Project-UIDB/50009/2020), Portuguese Foundation for Science and Technology (FCT) for support through Projeto Estategico LA 9--UIDB/50009/2020, ARDITI--Agencia Regional para o Desenvolvimento da Investigacao, Tecnologia e Inovacao under the scope of the project M1420-09-5369-PSE-000002--Post-Doctoral Fellowship, co-financed by the Madeira 14-20 Program--European Social Fund. This acknowledgment extends to NAV Portugal for their contribution of wind observation data from the Madeira International Airport.