## 4 Results and Discussion  
In this section, Table 3 provides the classification results obtained for using the whole set of 12 features. The considered algorithms are, in order, the DT, KSVM (KS in the Tables), NN, and RF. The results are described in terms of the specificity, recall, and F1-Score using the improved TBRG as a reference and after applying the leave-one-out approach where in each round one day was selected for testing and the remaining ones were used for training. The last two rows of the table represent the average and the standard deviations of the metrics on the 18 days. Table 4 reports similar results, obtained in this case using the reduced set of six features (from x1 to x6). \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**ID**} & \multicolumn{3}{c}{**Specificity**} & \multicolumn{3}{c}{**Recall**} & \multicolumn{3}{c}{**F1-Score**} \\ \hline  & **DT** & **KS** & **NN** & **RF** & **DT** & **KS** & **NN** & **RF** & **DT** & **KS** & **NN** & **RF** \\ \hline
1 & 0.87 & 0.90 & 0.89 & 0.92 & 0.84 & 0.72 & 0.97 & 1.0 & 0.49 & 0.48 & 0.58 & 0.67 \\
2 & 0.91 &  
Figure 3 presents the boxplots of the metrics shown in Tables 3 and 4. The lower and upper parts of the boxes delimit the first and third quartiles, while the whiskers mark the variability outside the quartiles. A red diamond symbol represents the average value of each metric, while blue horizontal lines represent the median values, respectively. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**ID** & \multicolumn{3}{c}{**Specificity**} & \multicolumn{3}{c}{**Recall**} & \multicolumn{3}{c}{**F1-Score**} \\ \hline  & **DT** & **KS** & **NN** & **RF** & **DT** & **KS** & **NN** & **RF** & **DT** & **KS** & **NN** & **RF** \\ \hline
1 & 0.87 & 0.82 & 0.85 & 0.89 & 0.93 & 0.86 & 0.98 & 0.99 &  
By looking at the classification performance reported in Table 3 and in the associated Figure 3a, we can notice that NN and RF present the best trade-off among the three metrics. NN outperforms RF in the recall metric, meaning that it is better at classifying rainy observations, while RF achieves a higher specificity, i.e., it classifies the non-rainy data more accurately. DT has the highest specificity but the lowest recall, thus it is not suitable for the classification of rainfall. Observing the F1-Score results, which represent the harmonic mean for precision and recall, it is noticeable that the RF and NN classifiers perform better than the DT and KSVM classifiers. An important comment should be raised, explaining that if the rain gauge detects rainfall, it is a sufficient condition for a rain occurrence over the SRS link, while the opposite is not true (i.e., if the rain gauge does not detect rainfall, it can occur elsewhere over the link, due to its extension). Consequently, the specificity metric, which gains lower levels, is less meaningful than the recall one, which actually presents very good values. Moreover, it is worth noting that adopting a subset of features (i.e., six features in this work), NN and RF still present the best trade-off with respect to the other classifiers. In particular, the average recall of NN is equal to the one obtained by using all 12 features, but the variability of the results is slightly higher. Meaning a small increase in the misclassification rate of rainy observations. On the other hand, the variability of NN specificity is smaller, with an average value of 1% better than by adopting 12 features. The specificity of RF decreases when using six features, while the recall increases. In general, reducing the number of features and maintaining a reasonable quality of the results, is very important within the machine learning approach, leading in fact to less complex models with short processing time and smaller storage capacity needs. This allows for reducing response time, which is a very crucial aspect of rainfall monitoring systems, in particular from the perspective of the implementation of such algorithms according to the edge computing paradigm.