## 5 Conclusions  
In the present work, we have used and compared several already established ML algorithms for monthly rainfall prediction. The performance comparisons and potential applications of learning machines are presented in this paper. This proposal presents very accurate and interpretable predictive models. This research is motivated by the idea of making the process of rainfall prediction simpler and more effective, as well as to overcome the difficulties that other proposals entail. Therefore, the main contributions of this paper are the following: (i) obtaining and comparing rainfall predictive models using several machine learning techniques and (ii) assessing whether the combination of local meteorological variables, NAO index, and the algorithms used has an effect on accuracy of the predictive models. The power of machine learning seems to be an efficient focus for the prediction of meteorological patterns such as rain. The results show that, despite what has been maintained by different authors, in complex orographic geographical areas, global variables such as the North Atlantic Oscillation Index (NAO) have a very low influence on the predictive model obtained, and local variables such as Geopotential Height (GPH) are relatively more important than local variables measured in meteorological stations.  
Seasonality predictors have not been applied in the predictive models in order to avoid possible influence on climatology. Machine learning models for rainfall prediction in regions with similar complex orography may also perform excellently. The following aspects will be analyzed in the future. Firstly, long short-term memory (LSTM) networks seem to be suitable for predictions based on time series data, since there can be lags of unknown duration between important events in these series. Secondly, Machine Learning for Streams, through monitoring streaming data from weather stations, is currently one of the most successful applications to forecast data sets in real time. This could be another research line to be explored.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Gbm** & \multicolumn{2}{c}{**XGBoost**} & \multicolumn{2}{c}{**Rf**} & \multicolumn{2}{c}{**Glm**} \\ \hline _GPH_ & 100 & _GHP_ & 100 & _Rha_ & 100 & _SST_ & 100 \\ _Rha_ & 98.40 & _Rha_ & 51.97 & _GHP_ & 86.36 & _GHP_ & 87.67 \\ _Wa_ & 31.49 & _Rhs_ & 49.58 & _Ta_ & 41.93 & _Ws_ & 63.03 \\ _Ta_ & 19.72 & _SST_ & 39.40 & _Rhs_ & 40.56 & _Ps_ & 8.11 \\ _Rhs_ & 19.22 & _Wa_ & 32.23 & _Wa_ & 21.61 & _Ta_ & 7.33 \\ _SST_ & 17.43 & _NAO_ & 26.52 & _SST_ & 15.49 & _Rha_ & 5.24 \\ _Ps_ & 13.67 & _Ws_ & 22.93 & _NAO_ & 12.09 & _Wa_ & 3.16 \\ _Ws_ & 8.97 & _Ta_ & 16.46 & _Ps_ & 5.55 & _Rhs_ & 2.10 \\ _NAO_ & 0.00 & _Ps_ & 0.00 & _Ws_ & 0.00 & _NAO_ & 0.00 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Scaled values of the relevance of predictors for gbm, XGBoost, rf, and glm models.  
**Author Contributions:** All the authors have contributed equally to the realization of this work. [PERSON], [PERSON], and [PERSON] participated in the conception and design of the work; [PERSON], [PERSON], and [PERSON] reviewed the bibliography; [PERSON], [PERSON], and [PERSON] conceived and designed the experiments; [PERSON], [PERSON], and [PERSON] performed the experiments; [PERSON], [PERSON], and [PERSON] analyzed the data; [PERSON], [PERSON], and [PERSON] wrote and edited the paper.  
**Acknowledgments:** This work has been possible thanks to the collaboration and support of the University Institute of Intelligent Systems and Numeric Applications in Engineering (IUSIANI-ULPGC).  
**Conflicts of Interest:** The authors declare no conflicts of interest.