## 6 Experimental Results
### Results  
This section presents the experimental results obtained by applying \(NowDeepN\) approach on the data set described in Section 6.1. For the DNNs used in our experiments, the implementation from the Keras deep learning API [50] using the Tensorflow neural networks framework was employed. The code is publicly available at Reference [51]. Given the fact that our data was quite high-dimensional, as mentioned in Section 6.1, we needed a relatively complex neural network. Each of The DNN regressors in the ensemble contains 12 hidden layer, with the following number of neurons: one layer with 200 neurons, one layer with 2000 neurons, 5 layers with 500 neurons and 5 layers with 100 neurons. These networks were trained for 30 epochs using 1024 instances in a training batch. As stated at the beginning of the paper, we aim to answer research question RQ1 by assessing the ability of \(NowDeepN\) to predict the values for the radar products at a given moment in a certain geographical location from the values of its neighboring locations from previous time moments. Besides, we intend to analyze how correlated are our computational findings with the meteorological evidence. Table 1 depicts the obtained results together with their 95% CI. The columns of the table illustrate the evaluation measures computed for all 13 products (second column), the average values computed for all six \(R\) products (third column), the average values computed for all six \(V\) products (fourth column) as well as for _VIL_ (fifth column). In order to allow an easier interpretation of the results from a meteorological perspective, we also illustrate in Table 1 the _Mean of Absolute Errors_ (the average of the absolute errors obtained for the testing instances) for all instances (MAE), as well as only for the non-zero labeled instances (MAE\({}_{non-zero}\)). From a meteorological point of view, the MAE for both all and non-zero instances is a satisfactory one, meaning that the predicted value is on the same level or on a neighbouring level on the product value scale. The following two figures depict a comparison between the real data gathered by the radar (Figure 9) and the prediction made by \(NowDeepN\) (Figure 10). The timestamp of the comparison was chosen so that there was significant meteorological activity (14:37 UTC is in the middle of the meteorological event, as described in the data set used in Section 6.1). The figures depict product R01, chosen because it is one of the most relevant radar products for nowcasting, as it provides information on the precipitation and usually contains more non-zero data than upper levels. Figure 9 represents the real data collected by the radar, while Figure 10 represents the predicted data, given the real data at 14:31:15 UTC. The figures represent the (real or predicted) values of R01 _over a geographical area_. Each pixel in the images represents a geographical location of roughly 1 km\({}^{2}\). The values on axes OX and OY represent the coordinates of _a pixel_ inside the image, relative to the (0,0) origin in the upper-left corner. While the values along the axes do not represent actual \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Evaluation** & **All 13** & **All R** & **All V** & **VIL** \\
**Measure** & **Products** & **Products** & **Products** & **VOL** \\ \hline MAE & 0.58 \(\pm\) 0.02 & 0.76 \(\pm\) 0.03 & 0.41 \(\pm\) 0.02 & 0.53 \(\pm\) 0.02 \\ \hline RMSE & 2.25 \(\pm\) 0.12 & 2.73 \(\pm\) 0.17 & 1.44 \(\pm\) 0.07 & 1.62 \(\pm\) 0.10 \\ \hline NRMSE & 3.27\% \(\pm\) 0.17\% & 3.91\% \(\pm\) 0.24\% & 2.15\% \(\pm\) 0.11\% & 2.32\% \(\pm\) 0.14\% \\ \hline MAE\({}_{non-zero}\) & 4.02 \(\pm\) 0.12 & 5.51 \(\pm\) 0.17 & 2.73 \(\pm\) 0.12 & 2.89 \(\pm\) 0.04 \\ \hline RMSE\({}_{non-zero}\) & 5.93 \(\pm\) 0.14 & 7.63 \(\pm\) 0.15 & 3.50 \(\pm\) 0.15 & 3.9 \(\pm\) 0.18 \\ \hline NRMSE\({}_{non-zero}\) & 8.60\% \(\pm\) 0.21\% & 10.91\% \(\pm\) 0.22\% & 5.22\% \(\pm\) 0.22\% & 5.63\% \(\pm\) 0.26\% \\ \hline \hline \end{tabular}
\end{table}