## 7 Discussion
### Comparison to Related Work
We also note that the evaluation from Reference [39] is performed only once, without using a cross-validation. [PERSON] et al. [31; 41] focused on their works on predicting the \(R\) values (using SVM and CNN classifiers), more specifically if they exceed 35 dBZ. Considering that the positive class is the one for which the \(R\) values are larger than 35, the authors used three evaluation measures: (1) _critical success index_ (CSI), computed as  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \begin{tabular}{c} **Feature** \\ **Extraction** \\ \end{tabular}  & \begin{tabular}{c} **Evaluation** \\ **Measure** \\ \end{tabular}  & \begin{tabular}{c} **All 13** \\ **Products** \\ \end{tabular}  & \begin{tabular}{c} **All \(R\)** \\ **Products** \\ \end{tabular}  & \ \begin{tabular}{c} **All \(V\)** \\ **Products** \\ \end{tabular}  & \begin{tabular}{c} **Improvement (\%)** \\ **without Feature** \\ **Extraction** \\ \end{tabular}  \\ \hline \multirow{4}{*}{AE} & RMSE & 2.40 \(\pm\) 0.07 & 2.93 \(\pm\) 0.08 & 1.51 \(\pm\) 0.05 & 1.76 \(\pm\) 0.11 & 6\% \\ \cline{2-7}  & NRMSE & 3.49\% \(\pm\) 0.10\% & 4.19\% \(\pm\) 0.12\% & 2.26\% \(\pm\) 0.08\% & 2.51  
\(CSI=\frac{TP}{TP+FN+FP}\); (2) _probability of detection_ (POD), \(POD=\frac{TP}{TP+FN}\); and (3) _false alarm rate_ (FAR), \(FAR=\frac{FP}{FP+TP}\). The MAR-CNN model proposed for precipitation nowcasting by [PERSON] et al. [42] used radar reflectivity images on three elevation levels and other numerical features and provided a RMSE of 7.90 for the predicted reflectivity values. Table 4 summarizes the comparison between \(NowDeepN\) and the related work. The best values for the evaluation measures are highlighted. Table 4 reveals that \(NowDeepN\) outperforms the approaches proposed by [PERSON] et al. [31; 41] in terms of CSI, POD and FAR evaluation measures. Overall, in 71% of the cases (5 out of 7 comparisons), the comparison is favorable to \(NowDeepN\). Our proposal is outperformed only by the work of [PERSON] [39] which reported a better HR and a maximum RMSE slightly better than ours. This difference may occur due to the following: (1) the data sets used (both as training and and testing) are different and have particularities due to the geographic area (country) on which were collected (i.e., China [39] and Romania); (2) the testing data set from Reference [39] contains data collected on a relatively small area which may lead to a biased evaluation and an overestimated performance. As previously mentioned, a lot of work has been carried out in the literature for precipitation nowcasting. [PERSON] and [PERSON] [40] tackled the precipitation nowcasting problem from a computer vision perspective, by applying certain thresholds on the reflectivity values (5/20/40 dBZ). In order to measure the performance of \(NowDeepN\) (in terms of CSI, POD and FAR) for the aforementioned thresholds we transformed the predicted values to predicted classes, by denoting each predicted value lower or equal to a threshold as being in the negative class and each predicted value higher than the threshold as being in the positive class. We then applied the same transformation on the ground truth and computed the measures; following this process for each of the three thresholds (5/20/40 dBZ). Table 5 illustrates the comparison between \(NowDeepN\) and the model proposed by [PERSON] and [PERSON] [40]. We mention that [PERSON] and [PERSON] [40] provide for each evaluation measure ranges of values. Since an exact comparison cannot be provided (the datasets used for evaluation and the input data models are different) our comparison relies only on the magnitude of CSI, POD and FAR evaluation metrics. The best values obtained for each reflectivity threshold and evaluation metric are highlighted. The comparative results from Table 5 highlight that \(NowDeepN\) obtained better results than the model proposed by [PERSON] and [PERSON] [40] in 77.7% of the cases (7 out of 9 comparisons).