# Performance Comparison of Machine Learning Disruption Predictors at JET
## 3 Models Implementation and Results
### MLP-NN Disruption Prediction Model
Figure 3: JET-disrupted discharge #94218: **(a)** Time evolution of the seven plasma dimensionless parameters: temperature \(\left(T_{pf}\right)\), plasma density \(\left(ne_{pf}\right)\), and radiated power \(\left(Rad_{pf-CVA}\right)\), and \(\left(\ Rad_{pf-XDIV}\right)\) peaking factors, internal inductance \(l_{i}\), radiated fraction of the total input power \(\left(P_{frac}\right)\), normalized locked mode amplitude \(\left(LM_{norm}\right)\) signal; **(b)** disruptive likelihood of the disrupted discharge #94218 supplied by MLP-NN. The dashed black line identifies the alarm time. Usually, a disrupted discharge evolves in the green region until disruption precursors appear, moving the trajectory towards the red disruptive region. The disruptive likelihood of the discharge is obtained by evaluating the percentage of the disrupted samples contained in the units visited by the trajectory. In our implementation, the validation set has not been used to evaluate the possibility of overfitting due to the huge computation time required. However, it has been instead joined to the training set to create and color the map. The free parameters of the GTM model, reported in Table 5, have been optimized with a Tabu Search procedure [44]. In Table 5, the resulting GTM map composition is also reported. The obtained GTM map of the JET operational space is reported in Figure 5a, where the same disrupted pulse #94218 is tracked. The trajectory of the discharge firstly evolves within the green \"safe\" region and then enters the red disruptive region. The lighter points of the trajectory correspond to the beginning of the discharge, whereas the darker one corresponds to the end at the disruption time \(t_{D}\). The corresponding disruptive likelihood is reported in Figure 5b. The vertical dashed line identifies the alarm time \(t_{alarm}\). Figure 4: 2-D GTM map of a n-D space, colored on the basis of the unit composition, and trajectory of a disrupted pulse. The color bar shows the time evolution of the discharge, and its color darkens during the time up to the tip of the arrow representing the end point. All the synthesized features except \(LM_{norm}\), used to train the MLP-NN, have been used also to train the GTM model. \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Value** \\ \hline Optimizer & Expectation Maximization \\ Map dimension & 50 \(\times\) 50 grid \\ Type of RBF & Radially symmetric \\ Number of RBF & 400 \\ Width \(\sigma\) of the RBF & 0.8 \\ Alarm threshold & 100\% \\ Log likelihood & 9.85 \(\times\) 10\({}^{5}\) \\ Disrupted units & 50.48\% \\ Non-disrupted units & 28.16\% \\ Mixed units & 18.52\% \\ Empty clusters & 5.48\% \\ Mean assertion time (ms) & 20 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: GTM training parameters. The disruptive likelihood usually has a discontinuous trend with numerous peaks that could trigger incorrect alarms if an adequate threshold and assertion time were not optimized. Moreover, the normalized locked mode signal, not used to train the GTM model, is used in the multiple condition alarm scheme shown in Figure 6, as proposed in [8]. The assertion time \(t_{assertion}\) is defined here as the time that the predictor waits before activating the alarm from the moment the disruptive likelihood exceeds the prefixed threshold. Note that the assertion time \(t_{assertion}\) dynamically varies during the discharge. As the sampling time is assumed equal to 2 ms, a mean assertion time of 20 ms is obtained in the entire dataset. For the disrupted discharge #94218 in Figure 5, the GTM correctly predicts the disruption with a resulting warning time \(\Delta t_{warning}=410\) ms.  
Table 6 reports the confusion matrix (green: successful results; red: mistakes) and the values of the same prediction performance indices reported in Table 4. The recall is very high, which means a very high percentage of successful disruption predictions (97.22% in the test), but the specificity degrades compared to the MLP-NN due to the greater number of false alarms.