# An application of survival analysis to disruption prediction via Random Forests

## TL;DR

This paper integrates survival analysis with machine learning, specifically Random Forests, to improve disruption prediction in tokamak plasmas, providing better warning times and reducing false alarms.

## Contribution

It introduces a novel application of survival analysis to disruption prediction, combining it with Random Forest classifiers for enhanced warning time estimation.

## Key findings

- Survival analysis improves disruption warning times.
- The method reduces false alarms compared to threshold-based schemes.
- Application to Alcator C-Mod data demonstrates practical effectiveness.

## Abstract

One of the most pressing challenges facing the fusion community is adequately mitigating or, even better, avoiding disruptions of tokamak plasmas. However, before this can be done, disruptions must first be predicted with sufficient warning time to actuate a response. The established field of survival analysis provides a convenient statistical framework for time-to-event (i.e. time-to-disruption) studies. This paper demonstrates the integration of an existing disruption prediction machine learning algorithm with the Kaplan-Meier estimator of survival probability. Specifically discussed are the implied warning times from binary classification of disruption databases and the interpretation of output signals from Random Forest algorithms trained and tested on these databases. This survival analysis approach is applied to both smooth and noisy test data to highlight important features of the survival and hazard functions. In addition, this method is applied to three Alcator C-Mod plasma discharges and compared to a threshold-based scheme for triggering alarms. In one case, both techniques successfully predict the disruption; although, in another, neither warns of the impending disruption with enough time to mitigate. For the final discharge, the survival analysis approach could avoid the false alarm triggered by the threshold method. Limitations of this analysis and opportunities for future work are also presented.