A multidimensional linear model for disruption prediction in JET

The implementation of Machine Learning techniques can help to develop accurate disruption predictors. However, they may provide outcomes difficult to understand from a physics point of view due to their mathematical formulation. In this work an interpretable linear equation has been derived from a data-driven model. This developed predictor can be used for real-time forecasting and for the off-line analysis of the variables that contribute to the alarm triggering. To create the linear model, in addition to physic quantities, Time Increments (TIs) that represent the differences in the amplitude of a signal divided by a predefined time interval have been considered. To select the best subset of quantities to include (from the wide range of possible combinations of signals and TIs), Genetic Algorithms have been applied. The results, obtained over an independent testing database of 131 unintentional disruptive and 1310 non-disruptive shots, are 99,24% of success rate (94,66% of them with at least 10 ms of warning time) and 3,51% of false alarms.