Data-driven disruption prediction using random forest in KSTAR

Abstract

Here, we develop a data driven disruption prediction model using the random forest algorithm using a database of KSTAR disruption shots. A total of 719 disruption shots from the past 3 campaigns have been selected to generate a database, which are terminated during the plasma current flattop phase by plasma physics issues and have all the essential diagnostic data. To train and test our machine learning model, the input features known to be associated with plasma disruption were collected from various diagnostic and analysis data. The slice datasets were labeled as ‘disruption’ less than 40 ms from thermal quench, and the remainder as ‘non-disruption’. The random forest-based disruption prediction model was trained and tested against the database. An out-of-bag (OBB) error indicates the optimal value of the ‘number of trees’ in the random forest model. A confusion matrix with test database shows an overall accuracy of 91 % in both phases, and an F-1 score of 0.915. This model shows the characteristics of disruptions that occur in KSTAR, and it can be helpful for disruption prediction studies of future superconducting tokamaks.