Gu, Y., Wei, H.-L. orcid.org/0000-0002-4704-7346, Boynton, R.J. et al. (2 more authors) (2017) Prediction of Kp Index Using NARMAX Models with A Robust Model Structure Selection Method. In: Electronics, Computers and Artificial Intelligence (ECAI), 2017 9th International Conference on. 2017 9th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), 29 Jun - 01 Jul 2017, Targoviste, Romania.
Abstract
The severity of global magnetic disturbances in Near-Earth space can crucially affect human life. These geomagnetic disturbances are often indicated by a Kp index, which is derived from magnetic field data from ground stations, and is known to be correlated with solar wind observations. Forecasting of Kp index is important for understanding the dynamic relationship between the magnetosphere and solar wind. This study presents 3 hours ahead prediction for Kp index using the NARMAX model identified by a novel robust model structure detection method. The identified models are evaluated using 4 years of Kp data. Overall, the models with robust structure can produce very good Kp forecast results and provide transparent and compact representations of the relationship between Kp index and solar wind variables. The robustness and conciseness of the models can highly benefit the space weather forecast tasks.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Space weather; Kp forecast; NARMAX model; Robust model; Structure selection |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 08 May 2018 09:03 |
Last Modified: | 21 Dec 2022 15:20 |
Published Version: | https://doi.org/10.1109/ECAI.2017.8166414 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1109/ECAI.2017.8166414 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:130274 |