Multi-Hour Ahead Dst Index Prediction Using Multi-Fidelity Boosted Neural Networks

@article{Hu2022MultiHourAD,
  title={Multi-Hour Ahead Dst Index Prediction Using Multi-Fidelity Boosted Neural Networks},
  author={Anna Hu and Enrico Camporeale and Brian M. Swiger},
  journal={ArXiv},
  year={2022},
  volume={abs/2209.12571}
}
The Disturbance storm time ( Dst ) index has been widely used as a proxy for the ring current intensity, and therefore as a measure of geomagnetic activity. It is derived by measurements from four ground magnetometers in the geomagnetic equatorial regions. We present a new model for predicting Dst with a lead time between 1 and 6 hours. The model is first developed using a Gated Recurrent Unit (GRU) network that is trained using solar wind parameters. The uncertainty of the Dst model is then… 

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