# Performance Comparison of Machine Learning Disruption Predictors at JET  
[PERSON]  
1 Department of Electrical and Electronic Engineering, University of Cagliari, 09123 Cagliari, Italy 1,*(c), [PERSON]  
1 Department of Electrical and Electronic Engineering, University of Cagliari, 09123 Cagliari, Italy 1,*(c), [PERSON]  
1 Department of Electrical and Electronic Engineering, University of Cagliari, 09123 Cagliari, Italy 1,*(c), [PERSON]  
1 Department of Electrical and Electronic Engineering, University of Cagliari, 09123 Cagliari, Italy 1,*(c), [PERSON]  
3 UK Atomic Energy Authority, Culham Science Centre, Abingdon OX14 3 DB, UK 3,*(c),
*(c),  
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c),  
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different devices. However, the presently available first-principles DP models, even if they are able to describe the relevant physics of disruptions in tokamaks in great detail [2], are not sufficiently fast to be run in real-time. On the contrary, a lot of data-driven models have been proposed as a viable solution to the DP problem, developed for several tokamaks all over the world, such as the JET (EU) [3; 4; 5; 6; 7; 8; 9], AUG (DE) [10; 11; 12], DIIID, NSTX, and c-Mod (USA) [13; 14], EAST and J-TEXT (CN) [14; 15], JT-60U (JP) [16], and ADITYA (IN) [17], many of which have been implemented and are currently operating in the control systems of the devices. This large number of proposals is due to the availability of both data from a lot of experimental campaigns performed in the different experimental fusion devices and of a lot of data-driven prediction approaches, such as statistical or machine learning methods [18]. Due to much larger dimensions than existing tokamaks, next generation devices, such as ITER and DEMO in Europe or CFTER in China, when operated at the full plasma current will not be able to withstand the stresses coming from unmitigated disruptions. Even if the performance of the proposed DP models seems quite good, no experimental data will be available on which to build data-driven models. For this reason, an integrated multi-machine approach is foreseen where existing facilities of different capabilities, sizes, and diagnostics will be used to provide DP models for these future fusion energy plants. Such an integrated approach needs to have different choices: (i) the choice of a common set of diagnostic signals, which are available in all the machines and in real time, and from which the best disruption prediction performance is obtained; (ii) the choice of the data-driven approach, better suited to the nature of the available data and to the disruption trigger to be provided; (iii) the choice of a common dataset, independent from the training set used to build the DP model, for testing the prediction performance; and (iv) the choice of common metrics to evaluate the model's performance, depending on the task, (avoidance, prevention, or mitigation). As previously cited, in the literature quite a large plethora of data-driven methods have been proposed: statistical [12; 19; 20], machine learning [10; 21; 22; 23] or, more recently, deep learning [24; 25]. Among them, the most commonly used are traditional neural networks such as MLP-NN [4; 10; 11; 15; 22] or support vector machines (SVM) [5; 6; 23; 26; 27; 28]), those belonging to manifold learning (self-organizing maps [29] and generative topographic mapping (GTM) [8; 9; 30]), decision trees [13; 21; 31] and deep neural networks [24; 32; 33; 34; 35]. Unfortunately, an accurate comparison of the different proposals in order to ultimately select the one that provides the best results for the specific application is prevented by the choice of different sets of diagnostic signals, different datasets for testing, even if the same device is considered, and different metrics to evaluate the performance of the models. In this paper, a first step toward the implementation of this integrated approach is presented.