Substorm Onset Prediction Using Machine Learning Classified Auroral Images

Abstract We classify all sky images from four seasons, transform the classification results into time‐series data to include information about the evolution of images and combine these with information on the onset of geomagnetic substorms. We train a lightweight classifier on this data set to predict the onset of substorms within a 15 min interval after being shown information of 30 min of aurora. The best classifier achieves a balanced accuracy of 59% with a recall rate of 39% and false positive rate of 20%. We show that the classifier is limited by the strong imbalance in the data set of approximately 50:1 between negative and positive events. All software and results are open source and freely available.