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\begin{document}

\title{A Prediction model of relativistic electrons at geostationary orbit
using the EMD-LSTM network and geomagnetic indices}



\author[1]{Hua Zhang}%
\author[1]{Haoran Xu}%
\author[2]{GuangShuai Peng}%
\author[3]{Ye dong Qian}%
\author[4]{Xiaoxin Zhang}%
\author[5]{Guanglin Yang}%
\author[6]{Chao Shen}%
\author[2]{Zheng Li}%
\author[3]{Jian wei Yang}%
\author[7]{Zhiqiang WANG}%
\author[8]{Fang He}%
\affil[1]{Institute of Space Weather, Nanjing University of Information Science \& Technology}%
\affil[2]{Nanjing University of Information Science \& Technology}%
\affil[3]{Najing university of Information Science \& Technology}%
\affil[4]{China Meteorological Administration}%
\affil[5]{National Forecasting Center for Space Weather}%
\affil[6]{School of Science, Harbin Institute of Technology}%
\affil[7]{Nanjing University of Aeronautics and Astronautics}%
\affil[8]{Polar Research Institute of China}%


\vspace{-1em}



  
  \date{June 11, 2024}


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\begin{abstract}
In this study, the Empirical Mode Decomposition algorithm (EMD) and the
Long Short Term Memory neural network (LSTM) were combined into an
EMD-LSTM model, to predict the variation of the \textgreater{}2MeV
electron fluxes. Input parameters include the Pc5 power and related
geomagnetic indices are used for predictions. As compared the prediction
results of the EMD-LSTM model with other classical prediction models,
the results show that the one-day ahead prediction efficiency of the
\textgreater{} 2MeV electron fluxes possesses a prediction efficiency of
0.80, and the highest prediction efficiency can reach 0.93. These
results are superior to the prediction accuracy of more traditional
models. Using two high-energy electron flux storm events for validation,
the results indicate that the performance of the EMD-LSTM model in the
period of the high-energy electron flux storm is also relatively good,
especially for the prediction of high-energy electron fluxes at extreme
points, and the prediction is closer to actual observation.%
\end{abstract}%



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