• The interaction between the solar wind and the Magnetosphere can produce Geomagnetically Induced Currents (GIC’s) on the ground, which are capable of causing power outages and damage to crucial infrastructure.
• The ability to predict when and where these events may occur could allow us to avoid the worst of this damage.
• The use of physics-based machine learning models can offer a computationally inexpensive method of predicting GIC events using dB/dt as a proxy, though most models thus far have fallen short of consistently accurate predictions.
• Here we train a Long-Short Term Memory (LSTM) machine learning model exclusively on geomagnetic storm occurring between 1995-2010, and the results are compared to those from a model trained on all data from those years.
