# Automated Aviation Wind Nowcasting: Exploring Feature-Based Machine Learning Methods
## 2 State of the Art  
Research has been channeled into developing accurate and efficient wind speed prediction models, underscoring the significance of feature selection and optimization techniques. Hence, a bibliographic review was undertaken to assess the leading feature-based methodologies in wind prediction using ML. [PERSON] et al. [12] adopted an innovative biologically inspired metaheuristic algorithm, coral reefs optimization (CRO), to process feature selection in wind speed prediction at wind farms. This method is a masterful simulation of coral reproduction and competition for space within an artificial reef, where each grid cell represents a potential solution. In this context, a key or \"coral\" is a unique set of meteorological variables capable of predicting wind speed. The CRO approach identified nine features that displayed robust performances across various regression models, thereby consolidating its efficacy in wind speed prediction feature selection. A critical work by [PERSON] et al. [13] introduced a wind speed prediction model employing the potent combination of a high-efficiency convex optimization support vector machine (SVM) and a reduced SVM (RSVM) for more tractable data regression. Their model integrated principal component analysis (PCA) to pinpoint critical factors impacting wind speed, thereby optimizing parameters. The RSVM-based model was validated using real-time data from wind power plants, thereby unmasking the potential of feature selection techniques such as PCA for enhanced model generalization. Wind speed, temperature, and pressure emerged as the key features that the model identified as necessary. Interestingly, the wind direction was found to have a minimal impact and was hence discarded. Consequently, the RSVM model, with PCA integration, stands as an exceptional leap forward in wind speed nowcasting. Moreover, Relief feature selection has also proven beneficial in wind speed nowcasting. As applied by [PERSON] and [PERSON] [14], this technique discerns essential features based on their relevance and contribution to the prediction task in a nonlinear autoregressive model process with exogenous input (NARX). The performance of the NARX model, when evaluated in terms of mean square error, accentuates the efficiency of the Relief feature selection, achieving greater performance when using the selected features for wind prediction: wind direction, humidity, and temperature. In 2016, [PERSON] et al. [15] proposed innovative models that amalgamate empirical mode decomposition (EMD), feature selection, artificial neural networks (ANN), and SVM for short-term wind speed prediction. After applying EMD to decompose the original wind speed time series into sub-series, these models identified the most informative features. Subsequently, predictive ANN or SVM models were constructed. Furthermore, a data-driven multi-model wind nowcasting methodology was developed by [PERSON] et al. [16], which utilized a two-layer ensemble machine learning technique with a deep feature selection framework to determine the most suitable inputs. The noteworthy improvements in nowcasting accuracy from these novel approaches further underscored the critical role of feature selection in enhancing wind speed prediction models. Yet another perspective was brought forth by [PERSON] et al. [17] with a feature selection framework rooted in mutual information. This was then paired with a stacked denoising autoencoder (SDAE) and an extended short-term memory network, based on a long short-term memory (LSTM), to distill intrinsic features and generate accurate predictions. When juxtaposed with traditional models, the superior performance of this SDAE-LSTM network highlighted the effectiveness of feature selection-driven hybrid deep architectures in wind speed prediction. A hybrid two-stage feature selection methodology was introduced by [PERSON] et al. [18], which merged filter- and function-based clustering models to extract valuable feature subsets.