Machine learning for disruption warnings on Alcator C-Mod, DIII-D, and EAST

This paper reports on disruption prediction using a shallow machine learning method known as a random forest, trained on large databases containing only plasma parameters that are available in real-time on Alcator C-Mod, DIII-D, and EAST. The database for each tokamak contains parameters sampled 106 times throughout 104 discharges (disruptive and non-disruptive) over the last four years of operation. It is found that a number of parameters (e.g. , , , ) exhibit changes in aggregate as a disruption is approached on one or more of these tokamaks. However, for each machine, the most useful parameters, as well as the details of their precursor behaviors, are markedly different. When the prediction problem is framed using a binary classification scheme to discriminate between time slices 'close to disruption' and 'far from disruption', it is found that the prediction algorithms differ substantially in performance among the three machines on a time slice-by-time slice basis, but have similar disruption detection rates ( 80%-90%) on a shot-by-shot basis after appropriate optimisation. This could have important implications for disruption prediction and avoidance on ITER, for which development of a training database of disruptions may be infeasible. The algorithm's output is interpretable using a method that identifies the most strongly contributing input signals, which may have implications for avoiding disruptive scenarios. To further support its real-time capability, successful applications in inter-shot and real-time environments on EAST and DIII-D are also discussed.