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\begin{document}

\title{72-hour Time Series Forecasting of hourly Relativistic Electron Fluxes
at Geostationary Orbit by Deep Learning}



\author[1]{Jihyeon Son}%
\author[1]{Yong-Jae Moon}%
\author[1]{Seungheon Shin}%
\affil[1]{Kyung Hee University}%


\vspace{-1em}



  
  \date{June 07, 2024}


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\begin{abstract}
In this study, we forecast hourly relativistic (\textgreater{}2 MeV)
electron fluxes at geostationary orbit for the next 72 hours using a
deep learning model. For this we consider three deep learning methods,
such as multilayer perceptron (MLP), LSTM, and sequence-to-sequence
based on LSTM. The input data of the model are solar wind parameters
(temperature, density and speed), interplanetary magnetic field
(\textbar{}B\textbar{} and Bz), geomagnetic indices (Kp and Dst), and
electron fluxes themselves. All input data are hourly averaged ones for
the preceding 72 consecutive hours. We use electron flux data from
GOES-15 and -16, and perform cross-calibration to match the two data.
Total period of the data is from 2011 January to 2021 March (GOES-15
data for 2011-2017 and GOES-16 data for 2018-2021). We divide the data
into training set (January-August), validation set (September), and test
set (October-December) to consider the solar cycle effect. Our main
results are as follows. First, the MLP model, which is the best,
successfully predicts hourly electron fluxes for the next 72 hours.
Second, root-mean-square error (RMSE) of our model is from 0.18 (for 1h
prediction) to 0.68 (for 72h prediction), and prediction efficiency (PE)
is from 0.97 to 0.53, which are much better than those of the previous
studies. Third, our model well predicts both diurnal variation and
sudden increases of electron fluxes associated with fast solar winds and
interplanetary magnetic fields. Our study implies that the deep learning
model can be applied to forecasting long-term sequential space weather
events.%
\end{abstract}%



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