Abstract
The rapid enhancement of high-energy electron flux affects the safe operation of satellites in the synchronous orbit area, so accurate forecast of flux is a key focus in space weather research. This study uses >2 MeV electron flux, solar wind parameters, and geomagnetic parameters from 2001 to 2006 to perform a delayed analysis of input parameters and establish a prediction model named gaussian process regression (GPR) based on machine learning, where sets of 2001-2005 are used as training and sets from January to December in 2006 as testing. It is shown that the GPR is found to have a better performance when comparing with four typical and widely used models: RDF, Low-E, FLUXPRED, and REFM. It also outperforms other intelligence models like backward propagation neural network (BPNN), support vector machine regression (SVR), decision tree regression (DT), and long short-term memory network (LSTM) in terms of flux forecast. The prediction efficiency (PE), correlation coefficient (R), and root mean square error (RMSE) of our GPR model are 0.83, 0.91, and 0.39, respectively. The validation of GPR is further verified by its relatively better prediction of extreme disturbed events in which electron flux suddenly increased or decreased by several orders of magnitude.
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Data Availability
>2 MeV high-energy electron flux is from GOES satellite and can be downloaded from the website of NOAA (https://ngdc.noaa.gov). The geomagnetic data comes from the Japan Memanbetsu station of the World Geomagnetic Data Center (http://wdc.kugi.kyoto-u.ac.jp/). The solar wind data is the data observed by the ACE spacecraft at the interplanetary L1 point (http://cdaweb.gsfc.nasa.gov/cdaweb/).
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Funding
This work was supported by the National Key R&D Program of China (2021YFA0718600), and the National Natural Science Foundation of China (Grant Nos. 42030203, 41931073, 41974190, and 42130202).
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Conceptualization and methodology, J. Y. Lu, H. Zhang, and G. S. Peng; Formal analysis and investigation, G. S. Peng and H. Zhang; Writing-original draft preparation, G. S. Peng and H. Zhang; Writing-review and editing, G. S. Peng, J. Y. Lu, H. Zhang, X. X. Zhang, G. L. Yang, Z. Q. Wang, C. Shen, M. Yi, and Y. H. Hao; All authors have read and agreed to the published version of the manuscript.
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Peng, G., Lu, J., Zhang, H. et al. Short-term forecast of high-energy electron flux based on GPR. Astrophys Space Sci 367, 89 (2022). https://doi.org/10.1007/s10509-022-04123-9
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DOI: https://doi.org/10.1007/s10509-022-04123-9