## 8 Conclusions and Future Work  
We introduced in this paper a supervised learning based regression model \(NowDeepN\), which used an ensemble of _deep artificial neural network_ for predicting the values for meteorological products at a certain time moment based on their historical values. \(NowDeepN\) was intended to be a proof of concept for the feasibility of learning to approximate a function between past values of the radar products extracted from radar observations and their future values. The \(NowDeepN\) model consisted of an ensemble of DNNs for radar products' values nowcasting. In this ensemble, a DNN model was used for learning to approximate the value of each radar product at a given time moment and a certain geographical location from the radar products' values from the neighborhood of that location at previous time moments. Experiments were conducted on real radar data provided by the Romanian National Meteorological Administration and collected on the Central Transylvania region. The obtained results provided an empirical evidence that in both normal and severe weather conditions the values for a radar product at a given moment in a certain location are predictable from the values of the neighboring locations from previous time moments. This evidence is essential for further using convolutional neural network models for automatically extracting from radar data features which would be relevant for predicting the radar products' values at a certain time moment based on their historical values. A data cleaning step was introduced for correcting the erroneous input radar and its impact on increasing the predictive performance of the \(NowDeepN\) model was highlighted. In addition, the relevance of the features considered in the supervised learning task was empirically proven. More specifically, the experiments shown that the radar products' values from the neighboring area of a certain geographical location \(l\) at time \(t\)\(-\)1 are useful for predicting the radar products' values on location \(l\) at time \(t\). The experimental results highlighted that our \(NowDeepN\) model has a good performance particularly for high values of the reflectivity threshold, which indicate its ability to detect moderate and heavy precipitation and medium and large hail. While from a meteorological point of view, the performance of \(NowDeepN\) for predicting radar reflectivity values one time step ahead is satisfactory, in order to assess its performance compared to techniques currently employed in weather nowcasting, further development of the model for multiple time steps (e.g., 5 or 10 time steps, covering 30 or 60 min) is needed. The experimental evaluation of \(NowDeepN\) will be further extended by enlarging the data set used for training the model. As future plans we aim to investigate convolutional neural network models [53] as well as supervised classifiers based on _relational association rule mining_[54; 55] for detecting relationships between the meteorological products' values which may distinguish between normal and severe meteorological phenomena. In addition, we will analyze the possibility to extend the features used in the learning process, by combining radar data with other features (e.g., geographic and antropic features). Conceptualization, G.C. and A.M.; methodology, G.C. and A.M.; software, G.C. and A.M.; validation, G.C., A.M. and E.M.; formal analysis, G.C. and A.M.; investigation, G.C., A.M. and E.M.; resources, G.C., A.M. and E.M.; data curation, G.C., A.M. and E.M.; writing-original