  Geomagnetically Induced Currents (GICs) are one of the most hazardous effects
of space weather. The rate of change in ground horizontal magnetic component
dBH/dt is used as a proxy measure for GIC. In order to monitor and predict
dBH/dt, ground-based fluxgate magnetometers are used. However, baseline
correction is crucial before such magnetometer data can be utilized. In this
paper, a low-cost Machine Learning (ML) enabled magnetometer system has been
implemented to perform realtime baseline correction of magnetometer data. The
predicted geomagnetic components are then used to derive a forecast for dBH/dt.
Two different ML models were deployed, and their real-time and offline
prediction accuracy were examined. The localized peaks of the predicted dBH/dt
are further validated using binary event analysis.
