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

TL;DR

This paper introduces a multi-fidelity boosted neural network model for predicting the Dst index 1 to 6 hours ahead, improving accuracy and uncertainty estimation over existing models using solar wind data.

Contribution

It develops a novel multi-fidelity boosting approach combined with GRU networks and uncertainty quantification for improved Dst index prediction.

Findings

Predicts Dst 6 hours ahead with RMSE of 13.54 nT

Outperforms persistence and simple GRU models

Effectively reduces prediction uncertainty

Abstract

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 estimated by using the ACCRUE method [Camporeale et al. 2021]. Finally, a multi-fidelity boosting method is developed in order to enhance the accuracy of the model and reduce its associated uncertainty. It is shown that the developed model can predict $Dst$ 6 hours ahead with a root-mean-square-error (RMSE) of 13.54 $\mathrm{nT}$. This is significantly better than the persistence model and a simple GRU model.