Predicting the Geoeffectiveness of CMEs Using Machine Learning
This work addresses the need for accurate space weather forecasts to mitigate disruptions to telecommunications and power grids, though it is incremental as it applies existing methods to a specific domain.
The study tackled the problem of predicting whether coronal mass ejections (CMEs) will cause geomagnetic storms by experimenting with various machine learning models trained on solar onset parameters, achieving adequate hit rates despite dataset imbalances and limited variables.
Coronal mass ejections (CMEs) are the most geoeffective space weather phenomena, being associated with large geomagnetic storms, having the potential to cause disturbances to telecommunication, satellite network disruptions, power grid damages and failures. Thus, considering these storms' potential effects on human activities, accurate forecasts of the geoeffectiveness of CMEs are paramount. This work focuses on experimenting with different machine learning methods trained on white-light coronagraph datasets of close to sun CMEs, to estimate whether such a newly erupting ejection has the potential to induce geomagnetic activity. We developed binary classification models using logistic regression, K-Nearest Neighbors, Support Vector Machines, feed forward artificial neural networks, as well as ensemble models. At this time, we limited our forecast to exclusively use solar onset parameters, to ensure extended warning times. We discuss the main challenges of this task, namely the extreme imbalance between the number of geoeffective and ineffective events in our dataset, along with their numerous similarities and the limited number of available variables. We show that even in such conditions, adequate hit rates can be achieved with these models.