# Enhancing Weather Forecasting Integrating LSTM and GA
## 2 Related Work  
Several works have studied the importance and accuracy of weather forecasting in the literature. Regression and machine learning models are two of the main techniques used, and some of the work is described below. Regression models are a type of statistical model commonly used in forecasting techniques. In effect, statistical models use historical data to predict new values by adjusting the parameters of the model by analyzing the differences between the observed and predicted values [13]. Linear regression models analyze the linear relationship between dependent and independent variables, improving the accuracy and reducing the computational time of the model [14]. According to [PERSON] and [PERSON] [1], there are two main types of linear regression models: simple and multiple. Simple linear regression describes the linear relationship between one dependent variable and one independent variable. On the other hand, multiple linear regression (MLR) analyses the linear relationship between a dependent variable and more than one independent variable. In [PERSON] et al. [15], different regression models such as MLR or least absolute shrinkage and selection operator (LASSO) regression were proposed to continuously update weather and management data with future scenarios to obtain weekly crop yield forecasts. The results showed that the proposed model achieved accurate and timely crop yield predictions compared to machine learning algorithms. In [PERSON] et al. [16], a systematic review was presented that analyzed multiple linear regression (MLR), as well as partial least squares regression, and random forest regression, to estimate grape and wine production by predicting climatic conditions such as temperature or precipitation. Overall, the paper highlighted that the accuracy and reliability of regression models depend on the quality and quantity of data, the selection of appropriate predictors, and the validation of the model, but can be useful for estimating vineyard yield. Similarly, in the renewable energy context, regression methods are often used to predict climate variables. [PERSON] and [PERSON] [17] analyzed the short-term prediction of solar radiation and wind speed using regression models to assess the linear relationship between the variables. The proposed model can effectively capture the dependencies between the variables with an accuracy of 98.2%. Ridge regression models have been proposed in [18; 19] to predict solar radiation and wind speed. In the former, the main objective was to reduce the problems associated with the intermittency of these variables, thus reducing the associated forecast errors. The results showed that the proposed model had a correlation coefficient of 0.903 and was able to improve the accuracy and generalization of the forecasts. As regression methods are not as efficient at dealing with non-linear data and high variability, machine learning methods, in particular deep learning (DL), are emerging as a solution [20]. In fact, DL can automatically learn features from data, instead of considering traditional selection techniques [21]. Extreme gradient boosting (XGBoost) is a powerful and versatile machine learning algorithm that has demonstrated exceptional performance in various applications. It is particularly effective in modeling complex systems, achieving high prediction accuracy, and handling large-scale data. It has been successfully applied in various fields, including weather forecasting for agriculture and renewable energy production. [PERSON] et al. and [PERSON] [22; 23] demonstrated the effectiveness of XGBoost in improving the accuracy of short-term solar power and wind power forecasts. Another useful method is transformer networks. Transformer networks have been shown to significantly improve the accuracy of weather variable forecasts. In [24], a hybrid system was introduced, H-Transformer, which outperformed other models in predicting renewable energy production. [PERSON] et al.