## 2 Literature Review
Among the first type, it is worth highlighting the model proposed in [19], which proposes a DM algorithm for rainfall prediction over the monsoon period in the Indian peninsula combined with statistical techniques and which predicts rainfall in five categories. In [20], the authors compare the predictive characteristics with Markov Chains with extended rainfall prediction and six other ML algorithms and find very good results, which even allow for detecting correlations between different climates and their predictive accuracy. Models including image processing use neural networks, like in the model proposed in [21], which focuses on the prediction of rainfall in a local region over a short period of time using ML techniques and working with spatiotemporal datasets. In order to tackle this problem, the authors suggest the use of Recurrent Neural Networks (RNN) with a convolutional structure. This methodology is applied in rainfall prediction systems based on radar image analysis of rainfall over a certain period. In addition, in [22], the authors develop an approach based on spatial analysis with DM techniques to enable the deduction of the correlations and causalities between satellite images of the moving trajectories of Mesoscale Convective Systems (MCS) and heavy rainfall in Tibet. The proposed approach proves to be efficient by automatically analyzing large meteorological datasets to assist weather forecasting. In [23], a machine learning method algorithm based on support vector machine with dislocation of temporal variables (DSVM) was used to make short-term rain predictions. For this purpose, data from meteorological stations and satellite meteorological images were used. The results were validated using weather threat scores, showing good forecasts within 1 to 6 h.  
In [24], the authors present a new approach for predicting rainfall and runoff in river basins over the next two months. To do this, the authors propose a combination of wavelet transform and artificial neural network (WANN) that incorporates observed and predicted time series in the input structure. The comparison between the performance of the proposed WANN model and the performance of traditional WANN models through an uncertainty analysis reveals the superiority of the model proposed in this study. Finally, in [25], a machine learning algorithm based on random forest (RF) is used to make quantitative estimates of rainfall. Satellite temperature data are combined with numerical weather prediction (NWP) data from the global forecast system. In general, this algorithm obtains better predictive results over ocean surfaces, underestimating the prediction of the rainfall rate over land. However, the selected RF classification model allows the precipitation area to be predicted with an accuracy of 0.87. In recent years, research has intensified in the analysis of weather prediction models, specifically of rainfall, both in the medium term and in the short term, using various techniques of Machine learning and Deep Learning [26; 27; 28; 29]. There exists a notable trend in the experimentation of models based on neural networks. However, these models require a careful selection and preprocessing of the weather predictors chosen to fit the physical environment in which the study is conducted. This paper proposes an analytic solution based on machine learning techniques, which allows monthly rainfall prediction. The data used include mainly meteorological datasets available from meteorological stations of the island of Tenerife and reanalysis values from NOAA databases.