"Forecasting the Disturbance Storm Time Index with Bayesian Deep Learni" by Yasser Abduallah, Jason T.L. Wang et al.
 

Forecasting the Disturbance Storm Time Index with Bayesian Deep Learning

Document Type

Conference Proceeding

Publication Date

1-1-2022

Abstract

The disturbance storm time (Dst) index is an important and useful measurement in space weather research. It has been used to characterize the size and intensity of a geomagnetic storm. A negative Dst value means that the Earth’s magnetic field is weakened, which happens during storms. In this paper, we present a novel deep learning method, called the Dst Transformer, to perform short-term, 1-6 hour ahead, forecasting of the Dst index based on the solar wind parameters provided by the NASA Space Science Data Coordinated Archive. The Dst Transformer combines a multi-head attention layer with Bayesian inference, which is capable of quantifying both aleatoric uncertainty and epistemic uncertainty when making Dst predictions. Experimental results show that the proposed Dst Transformer outperforms related machine learning methods in terms of the root mean square error and R-squared. Furthermore, the Dst Transformer can produce both data and model uncertainty quantification results, which can not be done by the existing methods. To our knowledge, this is the first time that Bayesian deep learning has been used for Dst index forecasting.

Identifier

85131116100 (Scopus)

Publication Title

Proceedings of the International Florida Artificial Intelligence Research Society Conference Flairs

External Full Text Location

https://doi.org/10.32473/flairs.v35i.130564

e-ISSN

23340762

ISSN

23340754

Volume

35

Fund Ref

National Aeronautics and Space Administration

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