Forecasting the Disturbance Storm Time Index with Bayesian Deep Learning

Authors

  • Yasser Abduallah NJIT
  • Jason T. L. Wang New Jersey Institute of Technology
  • Prianka Bose New Jersey Institute of Technology
  • Genwei Zhang New Jersey Institute of Technology
  • Firas Gerges New Jersey Institute of Technology
  • Haimin Wang New Jersey Institute of Technology

DOI:

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

Keywords:

disturbance, machine, dst, index, learning, deep, bayesian, cnn, lstm, network, aleatoric, epistemic, uncertainty, neural, time, storm

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.

Downloads

Published

04-05-2022

How to Cite

Abduallah, Y., Wang, J. T. L., Bose, P., Zhang, G., Gerges, F., & Wang, H. (2022). Forecasting the Disturbance Storm Time Index with Bayesian Deep Learning. The International FLAIRS Conference Proceedings, 35. https://doi.org/10.32473/flairs.v35i.130564

Issue

Section

Special Track: Neural Networks and Data Mining