# Automated Aviation Wind Nowcasting: Exploring Feature-Based Machine Learning Methods
## 3 Materials and Methods
### Methods
#### 3.2.3 Feature Selection  
Based on the state of the art, five feature selection techniques were chosen. The mRMR was chosen in alignment with the approach brought forth by [PERSON] et al. [17], which emphasized mutual information-based feature selection. PCA was adopted, inspired by the work of [PERSON] et al. [13], which effectively integrated it to identify influential factors affecting wind speed. RFECV was selected to consolidate the approach, emphasizing accuracy and efficiency, as noted in various studies [13; 14; 15; 16; 17; 18; 19; 20; 21]. The GA was employed due to its ability to emulate natural biological evolution processes, drawing parallels with the CRO method's efficacy in identifying robust features. Lastly, XGBoost, as highlighted by [PERSON] et al. [19], provided a compelling methodology for feature selection, leading to marked improvements in prediction accuracy metrics. These techniques can be grouped into four broad feature selection categories: filter/dimensionality reduction, wrapper, and embedded methods [40]. In Figure 5, a flowchart is presented, which points to the steps and procedures followed in this process. The feature selection process was carried out independently for each target variable and forecast window. This methodology was implemented to ensure an in-depth and varied understanding of the significance of each feature across distinct prediction scenarios. By isolating the process for every target variable and forecast window, variations in feature importance across different prediction variables and time horizons were discerned. Such an approach provides a comprehensive view, revealing how certain features might hold more importance for one prediction context but may wane in relevance in another. The minimum redundancy maximum relevance (mRMR) and PCA techniques were employed as filter methods. The mRMR method, widely used for feature selection, brings inherent benefits, particularly its reduced computational complexity and processing speed. It works by selecting the features with high amounts of mutual information with the class but with minimum information shared between each other [41; 42]. The PCA method is one of the classic and most widely used techniques to reduce the dimension of a dataset without losing statistical information [43; 44]. The calculations were processed by Python 3 using pymrmr (version 0.1.11) and scikit-learn (version 1.2.2) libraries, and the number of features selected was based on the minimum mean absolute percentage error (MAPE) for each target and prediction window. In wrapper methods, the recursive feature elimination with cross-validation (RFECV) and the Genetic Algorithm (GA), which is also considered an evolutionary search-based method, were used. A regression tree was chosen due to its quick processing speed, simplicity, minimal input parameters, which reduce subjectivity and enhance reproducibility, and its explainability potential, which lends itself to further exploratory studies [45; 46; 47; 48; 49; 50]. Concerning the RFECV, a commonly used methodology that deletes features that may interfere with the result, data processing was performed using an algorithm that incorporated both the RFECV feature selection module and the decision tree regressor libraries, using a 5-fold cross-validation setup [51; 52; 53]. Regarding the GA, for optimization of the features involved, a Python script was developed to facilitate an evolutionary algorithm inspired by natural selection processes, emulating the process of natural biological evolution [54; 55]. The developed GA optimizes the decision tree regressor's feature selection using fitness scores derived from the MAPE and applied to a population of 100 individuals, in which each chromosomes had 56 genes. The fundamental mechanisms are the selection based on the best fitness individuals, multipoint crossover on reproducing steps, a mutation rate of 0.5%, and an early stopping criterion based on a lack of fitness improvement over ten generations.