# Performance Comparison of Machine Learning Disruption Predictors at JET
## 4 Discussion and Conclusions
It is possible to see how the CNN and the MLP have the best compromise between the detection of disruptions and the number of false positives (false alarms), which is also visible in the AUC reported in Table 9. Moreover, looking at the points of the ROC, it is possible to verify that the CNN performance on the test is more robust than that on the MLP-NN. In fact, both models have an optimal threshold above 0.9, but the CNN has an overall accuracy of 89% even with the lower thresholds, up to 0.7. It is possible to also confirm this remark by comparing the three disruptive likelihoods in Figures 3b, 4b and 6b. The CNN has a lower disruptive likelihood in the stable phase of the disruption, and then rises abruptly in the last part of the discharge, where it is possible to also see a clear variation in the images of the profiles. In the same Table 9, the assertion time of the three models are reported. Figure 10: Receiver operating curve (ROC) of the MLP-NN (blue line), the GTM (red line), and the CNN (green line) models. \begin{table}
\begin{tabular}{l c c} \hline \hline
**DP Model** & **AUC** & **Assertion Time (ms)** \\ \hline MLP-NN & 0.98 & 0 \\ GTM & 0.89 & 20 \\ CNN & 0.98 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: AUC and assertion time for MLP-NN, GTM, and CNN models. Note that the more recent campaigns at the JET contain a higher number of mitigated discharges. The approach for the inclusion of mitigated discharges is the adoption of the DMS activation time as the \(t_{D}\). This choice will unavoidably introduce an uncertainty in the classification of the discharge. However, since these shots are present in the test set only, we can notice that the predictors actually recognize the presence of disruption precursors before the valve activation, with a limited number of false alarms. Concluding, in recent years, a plethora of different machine learning models, plasma features, and performance metrics have been proposed for the development of disruption predictors. This work aims to provide a systematic comparison of some of the most adopted models and to select common metrics for the results evaluation. Using the same training and test set, an MLP-NN, a GTM, and a CNN have been trained as disruption predictors starting from the same set of plasma parameters: the electron temperature, density and radiation profiles, the locked mode signal, the radiated fraction of the total input power, and the internal inductance. The GTM and the MLP-NN have been trained using a set of processed signals developed from the plasma profiles, the peaking factors, while the CNN is able to directly process the spatiotemporal images of the diagnostics. Every evaluated method demonstrated the capability of producing early warning times and, in the case of the MLP-NN and of the CNN, with a reduced number of false alarms. Despite the GTM performances being a bit below the other two, its advantage is the interpretability of the model output and the possibility to quantify the distance of the tracked discharge from the non-disrupted area of the map. On the other hand, the CNN has the advantage of being able to process the input images without the need of extracting physics-based features due its capability to process image data. In the future, 1-D profiles or images coming from other diagnostics can be exploited. As an example, in [45], the radiation profiles coming from the vertical lines of sight of the bolometer have been used with very encouraging results. The lower interpretability of neural network models could be addressed by exploiting analysis algorithms such as class activation mapping [46] and by developing predictors which identify specific events.