Short-term prediction of geomagnetic secular variation with an echo state network

Abstract A technique for predicting the secular variation (SV) of the geomagnetic field based on the echo state network (ESN) model is proposed. SV is controlled by the geodynamo process in the Earth’s outer core, and modeling its nonlinear behaviors can be challenging. This study employs an ESN to represent the short-term temporal evolution of the geomagnetic field on the Earth’s surface. The hindcast results demonstrate that the ESN enables us to predict SV for a duration of several years with satisfactory accuracy. It is also found that the prediction is robust to the length of the the training data period. This suggests that the recent features of the SV are important for short-term prediction and that the ESN effectively learns these features. Graphical Abstract

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Publication Details

Journal
Earth Planets and Space
Published
2024-09-26
DOI
https://doi.org/10.1186/s40623-024-02064-x
Citations
2
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
0.69

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article

Short-term prediction of geomagnetic secular variation with an echo state network

Hiroaki Toh, S. Nakano, Sho Sato
2 citations
Earth Planets and Space
Neural Networks and Reservoir Computing
0.69
article

Short-term prediction of geomagnetic secular variation with an echo state network

Hiroaki Toh, S. Nakano, Sho Sato
article en
2 citations

Abstract

Abstract A technique for predicting the secular variation (SV) of the geomagnetic field based on the echo state network (ESN) model is proposed. SV is controlled by the geodynamo process in the Earth’s outer core, and modeling its nonlinear behaviors can be challenging. This study employs an ESN to represent the short-term temporal evolution of the geomagnetic field on the Earth’s surface. The hindcast results demonstrate that the ESN enables us to predict SV for a duration of several years with satisfactory accuracy. It is also found that the prediction is robust to the length of the the training data period. This suggests that the recent features of the SV are important for short-term prediction and that the ESN effectively learns these features. Graphical Abstract

Earth Planets and SpaceVol. 76(1)
The Graduate University for Advanced Studies, SOKENDAI (JP), Kyoto University (JP), The Institute of Statistical Mathematics (JP), Research Organization of Information and Systems (JP)
Research Organization of Information and Systems, Japan Society for the Promotion of Science
Openalex Percentile: Top 24%
Neural Networks and Reservoir Computing
0.69
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Short-term prediction of geomagnetic secular variation with an echo state network — Hiroaki Toh, S. Nakano, et al. · Earth Planets and Space (2024) | TGRS Research Map | TGRS