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Time Series Forecasting of Hourly Relativistic Electrons at Geostationary Orbit during Solar Cycles 23–24

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Abstract

Forecasting of the dynamically relativistic electrons is crucial to alert of the flux enhancements that pose a significant threat to satellite operation at geostationary orbit (GEO). This work investigates contribution of solar wind and geomagnetic activities in the hourly electron flux (E > 2 MeV) at GEO measured by GOES satellites during the solar cycles 23–24. Time series forecasting models based on three Base learners: linear regression, Sequential Minimal Optimization for Regression (SMOreg), and feed forward neural network (NN) scheme with back-propagation learning in Waikato Environment for Knowledge Analysis (WEKA) are adopted to predict the GEO electron fluxes (f). It is found that the computation time of SMOreg method is much longer than other methods. For 48-hour lagged times of short testing sets of 5 days, the prediction capability of all Base learners is fairly good and comparable, but the SMOreg model based on the auroral (electrojet) lower (AL) overlay on logarithmic flux (log-f) is more suitable for the forecasting particularly during years 2008, 2011, and 2015. Sometimes the ultra-low frequency (ULF) wave indices as inputs provide better prediction accuracy than the solar wind speed as found in 2008. The significance of the AL suggests the role of substorm activities on the relativistic electron enhancements. The results suggest to nonlinearity in short-term variations in the relativistic electron flux in relation to the solar wind and magnetospheric conditions. For a larger testing sets (4 months) with 1-day lagged time, the values of the mean absolute error (MAE) and root mean square error (RMSE) increase. All Base learners provide similar good prediction accuracy results for each year with lower values of MAE and RMSE during 2008 and 2011. The prediction accuracy obtained from the input of selected parameters to overlay on the log-f is similar to the input of the log-f only. The model results indicate a linear dependence of relativistic electron fluxes on the solar wind and magnetospheric conditions in a longer term.

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ACKNOWLEDGMENTS

This research was financially supported by Mahasarakham University 2020. The author is grateful to the GOES mission. The OMNI data were obtained from (http://omniweb.gsfc.nasa.gov/). The author also wishes to thank the reviewers for constructive comments that have led to notable improvement in the manuscript.

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  1. Department of Physics, Geoinformatics and Space Technology, Mahasarakham University, 44150, Kantarawichai, Maha Sarakham, Thailand

    Thana Yeeram

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Thana Yeeram Time Series Forecasting of Hourly Relativistic Electrons at Geostationary Orbit during Solar Cycles 23–24. Geomagn. Aeron. 62, 278–287 (2022). https://doi.org/10.1134/S0016793222030185

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