**4.1 Evaluation of the SOM, TKM and RSOM classifiers**

Table 1 shows the confusion matrices and the global accuracy of the neural networks studied. It is noticed that with the TKM and RSOM classifiers were provided superior performances to the original SOM, and between the recurrent networks, the RSOM network showed the best results.

Fig. 8 exhibits the ROC graph for the SOM, TKM and RSOM when the grids of the neural networks are 5 x 5. One may notice that the RSOM presents a larger tp rate and a smaller fp rate for the labels 2 and 3. For the label 1, the SOM network presented itself as the most liberal, and the RSOM as the most conservative network. Therefore, for this grid, the results have indicated that the RSOM classifier has a better performance than the other networks analyzed in this work.



Table 1. Evaluation of the SOM, TKM and RSOM classifiers in different grids

false positive rates are low but they also have low true positive rates. Liberal classifiers have

This section presents the results of the assessment among the studied networks: Self-Organizing Map (SOM), Temporal Kohonen Map (TKM) and Recurrent Self-Organizing

Table 1 shows the confusion matrices and the global accuracy of the neural networks studied. It is noticed that with the TKM and RSOM classifiers were provided superior performances to the original SOM, and between the recurrent networks, the RSOM network

Fig. 8 exhibits the ROC graph for the SOM, TKM and RSOM when the grids of the neural networks are 5 x 5. One may notice that the RSOM presents a larger tp rate and a smaller fp rate for the labels 2 and 3. For the label 1, the SOM network presented itself as the most liberal, and the RSOM as the most conservative network. Therefore, for this grid, the results have indicated that the RSOM classifier has a better performance than the other networks

Table 1. Evaluation of the SOM, TKM and RSOM classifiers in different grids

high true positive rates but they also have high false positive rates.

Map (RSOM) for the severe weather pattern classification.

**4.1 Evaluation of the SOM, TKM and RSOM classifiers** 

**4. Results** 

showed the best results.

analyzed in this work.

Fig. 8. ROC graph for the SOM, TKM and RSOM in 5x5 grid

Fig. 9 displays the ROC graph of the analyzed models for the 7x7 grid. It is evident that the RSOM network has a larger tp rate and a smaller fp rate for labels 1 and 2. For the label 3 the SOM and TKM networks presented similar liberal characteristics, while the RSOM network showed a more conservative behavior. For these dimensions the results also indicated a better performance of the RSOM classifier when compared to the SOM and TKM network options.

Fig. 9. ROC graph for the SOM, TKM and RSOM in 7x7 grid

Recurrent Self-Organizing Map for Severe Weather Patterns Recognition 165

Legend: Color scale represent the Euclidean distance between the codebook vector of the neighbouring

**4.3 Labeling of the neurons after the training of the SOM, TKM and RSOM networks**  Table 3 shows a comparison between the labeling of the neurons after the training process, using as criteria the activation frequency. It is noticed that RSOM network has a higher organization when compared with the other networks. The labels: blue for the cluster 1,

Table 2. U-matrix of the SOM, TKM and RSOM networks

green for the cluster 2, and red for the cluster 3, were used.

neurons

The ROC graph for the SOM, TKM and RSOM in 9x9 grid is presented in Figure 10. One notices that for this grid, the RSOM network has a larger tp rate and smaller fp rate for all three labels considered. This fact confirms even more the best performance observed for the RSOM classifier, among all networks analyzed.

Fig. 10. ROC graph for the SOM, TKM and RSOM in 9x9 grid
