The Possibility of Real-Time and Long-Term Predictions for Geomagnetic Storms Using Neural Networks
Abstract
Two backpropagation neural network (BPNN) models were constructed to predict two historic geomagnetic storms that occurred in September 1999 and October 2003. The disturbance storm time (Dst) indices from January 1, 1999, to December 31, 2014 (coordinated universal time, UTC), were used as the training and test data sets for cross-validation in order to verify and validate the reliability and robustness of the two BPNN models, and yielded reasonable, predicted results. A large correlation coefficient (R) and low root mean square error (RMSE) were obtained, verifying the reliability of the two BPNN models. The predicted Dst indices can be provided for giving inputs in advance (i.e., any future time). Therefore, this analyzed method can serve as an excellent real-time prediction (RTP). To test the ability and possibility of the long-term prediction (LTP) obtained using the two BPNN models, the Dst indices were examined, which corresponded to two significant historic large geomagnetic storms that occurred in August 1972 and March 1989. For the both BPNN models, after evaluating their learning procedure, the time-dependence of LTP, the dependence of the predicted errors on the time period length of training data sets, and the variance by learning process, we found that they were stable models for the RTP and LTP.
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Data Availability Statement
Source of the Dst indices: http://wdc.kugi.kyoto-u.ac.jp/dst_final/index.html. Therefore, the author thanks the World Data Center for Geomagnetism, Kyoto for the data support. The Matlab code source can be found here: https://web.fs.uni-lj.si/lasin/en/pedagoski-proces/nevronske-mreze/. The author corrected the code for the purposes of this study. The author also thanks the support by Department of Electrical Engineering, Southern Taiwan University of Science and Technology.
Acknowledgments
The author thanks the World Data Center for Geomagnetism, Kyoto, M. Nose, T. Iyemori, M. Sugiura, T. Kamei (2015), Geomagnetic Dst index, doi: 10.17593/14515-74000. The author is grateful to the geomagnetic observatories [Kakioka (JMA), Honolulu and San Juan (USGS), Hermanus (RSA), INTERMAGNET] and many others for their cooperation to make the final Dst index available. The author is grateful to Department of Electrical Engineering, Southern Taiwan University of Science and Technology. The author is also grateful to his mother who passed away in 2016.
Author contributions: Jyh-Woei Lin designed the study, performed the study, analyzed the data, and wrote the paper.
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Information & Authors
Information
Published In
Natural Hazards Review
Volume 24 • Issue 3 • August 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Oct 3, 2022
Accepted: Feb 16, 2023
Published online: Apr 28, 2023
Published in print: Aug 1, 2023
Discussion open until: Sep 28, 2023
ASCE Technical Topics:
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- Engineering fundamentals
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- Meteorology
- Methodology (by type)
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- Precipitation
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- Statistics
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