## 7 Discussion
### Comparison to Related Work  
The literature review from Section 4 revealed various approaches developed in the nowcasting literature using machine learning methods. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Evaluation** & **All 13** & **All \(R\)** & **All \(V\)** & **VIL** & **Improvement (\%)** \\
**Measure** & **Products** & **Products** & **Products** & **(Cleaning Step)** \\ \hline RMSE & 4.98 \(\pm\) 0.06 & 4.97 \(\pm\) 0.10 & 3.99 \(\pm\) 0.07 & 5.24 \(\pm\) 0.17 & 55\% \\ \hline NRMSE & 7.27\(\%\)\(\pm\) 0.09\% & 7.10\(\%\)\(\pm\) 0.15\% & 5.95\(\%\)\(\pm\) 0.10\% & 7.49\(\%\)\(\pm\) 0.24\% & 55\% \\ \hline RMSE\({}_{non-zero}\) & 10.05\(\pm\) 0.40 & 9.38 \(\pm\) 0.23 & 10.88\(\pm\) 0.71 & 9.10 \(\pm\) 0.31 & 41\% \\ \hline NRMSE\({}_{non-zero}\) & 14.68\(\%\)\(\pm\) 0.59\% & 13.40\(\%\)\(\pm\) 0.33\% & 13.24\(\%\)\(\pm\) 1.06\% & 13.00\(\%\)\(\pm\) 0.44\% & 41\% \\ \hline \end{tabular}
\end{table} 
Table 2: Experimental results obtained using \(NowDeepN\) on the uncleaned data. 95% CI are used for the results. The last column illustrates the improvement achieved applying \(NowDeepN\) on the cleaned data, considering all 13 radar products. We start the comparison between \(NowDeepN\) and related work by comparing our model to a simple baseline model, the _linear regression_ (LR). For an exact comparison, the data model used for \(NowDeepN\) (Section 5.2) was used for the LR model as well. By applying the LR on the dataset described in Section 6.1, an overall RMSE for the non-zero values (\(\text{RMSE}_{non-zero}\)) of 6.094 was obtained. Surprisingly, there is only 3% improvement achieved by \(NowDeep\) on our data, with a higher improvement on \(V\) (about 12%). From a meteorological point of view, the minor improvement in reflectivity nowcasting (compared to the LR model) is probably determined by the fact that the model is predicting the values of the radar products for one time step, and on the particular day used in this study the convective structures detected by the radar display a relatively slow evolution due to the light to moderate wind, thus no rapid modifications between two radar scans can be observed in terms of \(R\) value or location. Bigger improvements can be observed in \(V\) product values predictions, since the evolution of this product has a more stochastic character. Future work, dealing with prediction over more time steps, should display greater improvements compared to the benchmark LR model. Even if there are numerous machine learning-based methods developed for nowcasting purposes, there are few methods focused on radar base products' values nowcasting, such as reflectivity nowcasting. Most of the related work focus on the precipitation nowcasting problem. We found four approaches having similar goal to our paper, that of predicting the future values of the radar products' values based on their historical values. The approaches from the literature which are the most similar to ours are those proposed by [PERSON] [39], [PERSON] et al. [31; 41] and [PERSON] et al. [42]. Even if the data sets used in the previously mentioned papers and the evaluation methodology differs from ours, we computed the evaluation measures reported in literature, trying to reproduce as accurately as possible the experiments from the related work. The work of [PERSON] [39] is focused on predicting only the reflectivity values which are further used for precipitation prediction. Experiments are performed only on radar data collected only for time stamps when storms occurred, disregarding the periods with normal weather (i.e., for which the \(R\) value is 0). Besides the minimum and the maximum values for the RMSE, the _Hit rate_ (HR) is reported in Reference [39] as the percentage of instances for which the absolute error (between the predicted and the real value) is less than or equal to 5. For an accurate comparison with the work of [PERSON] [39], we trained our \(NowDeepN\) model only on the instances labeled with non-zero values for \(R\) and was tested only on non-zero instances.