A Transformer-Based Framework for Geomagnetic Activity Prediction

Yasser Abduallah, Jason T.L. Wang, Chunhui Xu, Haimin Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


Geomagnetic activities have a crucial impact on Earth, which can affect spacecraft and electrical power grids. Geospace scientists use a geomagnetic index, called the Kp index, to describe the overall level of geomagnetic activity. This index is an important indicator of disturbances in the Earth’s magnetic field and is used by the U.S. Space Weather Prediction Center as an alert and warning service for users who may be affected by the disturbances. Early and accurate prediction of the Kp index is essential for preparedness and disaster risk management. In this paper, we present a novel deep learning method, named KpNet, to perform short-term, 1–9 hour ahead, forecasting of the Kp index based on the solar wind parameters taken from the NASA Space Science Data Coordinated Archive. KpNet combines transformer encoder blocks with Bayesian inference, which is capable of quantifying both aleatoric uncertainty (data uncertainty) and epistemic uncertainty (model uncertainty) when making Kp predictions. Experimental results show that KpNet outperforms closely related machine learning methods in terms of the root mean square error and R-squared score. Furthermore, KpNet can provide both data and model uncertainty quantification results, which the existing methods cannot offer. To our knowledge, this is the first time that Bayesian transformers have been used for Kp prediction.

Original languageEnglish (US)
Title of host publicationFoundations of Intelligent Systems - 26th International Symposium, ISMIS 2022, Proceedings
EditorsMichelangelo Ceci, Sergio Flesca, Elio Masciari, Giuseppe Manco, Zbigniew W. Raś, Zbigniew W. Raś
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages11
ISBN (Print)9783031165634
StatePublished - 2022
Event26th International Symposium on Methodologies for Intelligent Systems, ISMIS 2022 - Rende, Italy
Duration: Oct 3 2022Oct 5 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13515 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference26th International Symposium on Methodologies for Intelligent Systems, ISMIS 2022

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


  • Bayesian inference
  • Deep learning
  • Geomagnetic index
  • Uncertainty quantification


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