## 2 Literature Review
Among the literature reviewed, there are other specific models that range from models for the prediction of the number of solar irradiance hours in a specific geographic area [11], to models for the prediction of wind gusts for agriculture applications [12], or even models for the classification of the quality of wine in a specific region depending on meteorology [13]. In the study by [11], several ML methods are presented to obtain predictions of solar radiation through an analysis to compare the different margins of error in predictions. In order to improve the characteristics of the predicting algorithm, a series of hybrid models or ensemble models used by different authors are presented. In the paper by [12], authors study wind gust prediction. This forecasting is characterized by the high variability and brief duration of wind gusts and because they occur suddenly and end abruptly. This work suggests a pattern based on the application of ML techniques, which classifies those gusts by using thousands of measurements made using both real-time variables and variables from mathematical models. Finally, in [13], authors developed an application using DM techniques to predict wine quality depending on climatic, physical, and crop yield factors. In order to do so, an artificial neural network algorithm was used to classify the data associations and the chi-square test was used to establish the degree of dependence between the related variable values. In [14], the authors propose the integration of meteorological data from different sources of the so-called Data Mining Meteo (SMM) system, which makes it possible to obtain, by using DM techniques, quick predictions, even in the presence of randomly occurring changing phenomena. The applications focus on the short-term prediction of meteorological events such as fog and low cloud cover. Furthermore, in [15], the authors make cloud-ceiling-height forecast based on METAR meteorological data at JFK airport. In this work, the authors conclude that ML algorithms show better prediction results than methods based on Numerical Weather Prediction (NWP) models. In a different study [16], the authors propose an interesting multimodal algorithm for predicting visibility due to atmospheric conditions, not only related to meteorological variables, but also taking into account pollution from gaseous effluent contamination of factory exhaust in a given area. For this purpose, an advanced numerical prediction model and a method for detecting gaseous pollutant emissions are used. Two numerical regression algorithms, the XGBoost and the LightGBM, are used to train the prediction model, to which the estimation data based on Landsat-8 satellite images are added to help in the prediction. The results obtained by this numerical prediction model are more accurate than those obtained by other methods. In [17], the authors introduce a system to obtain accurate short-term predictions, which uses regression functions and data collected from weather stations, including temperature, wind speed, solar radiation and pressure, humidity, cloudiness, and rainfall. After preprocessing these data, the prediction results obtained are compared with SVM, regression tree (RF), and a fit linear model (FITLM). The results show the robustness of the system. In [18], the authors propose the use of a modified algorithm based on XGBoost for forecasting wind energy for use in the electrical system. The results of this model are compared with others, based on neural networks (BPNN), regression trees (CART), support vector regression (SVR), and, with a simple XGBoost model, obtaining the best accuracy results in the prediction. The rainfall estimation models suggested use either numerical historical data from meteorological stations or data from reanalysis software or from these combined with images.