Relativistic Electron Model in the Outer Radiation Belt Using a Neural Network Approach

Chu, Xiangning; Ma, Donglai; Bortnik, J.; Tobiska, W. Kent; Cruz, Alfredo; Bouwer, S. Dave; Zhao, Hong; Ma, Qianli; Zhang, Kun; Baker, D. N.; Li, Xinlin; Spence, H. E.; Reeves, G. D.

doi:10.1029/2021sw002808

Cited by 44 publications

(47 citation statements)

References 87 publications

(116 reference statements)

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“…Space weather forecasts are typically driven by data obtained upstream of the Earth at the L1 point (e.g., Baker et al., 1990; Chu et al., 2021; Forsyth et al., 2020; Keesee et al., 2020; Smith, Forsyth, Rae, Garton, et al., 2021; Wing et al., 2005), approximately 1.5 million km ahead of the Earth. Given the speed of the solar wind, this provides between 20 and 90 min of warning before solar wind plasma encounters the Earth (Baumann & McCloskey, 2021).…”

Section: Introductionmentioning

confidence: 99%

On the Considerations of Using Near Real Time Data for Space Weather Hazard Forecasting

et al. 2022

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Space weather has the potential to pose a severe threat to modern society. The Earth's magnetosphere is constantly buffeted by the solar wind that emanates from the Sun. It is the variable nature of the solar wind that leads to a highly dynamic environment in near-Earth space. There are many different facets to space weather, including the changing radiation environment in near-Earth space, a hazard that faces Earth orbiting satellites (Baker et al., 1987;Iucci et al., 2005), and strong ground magnetic field variability that can induce damaging currents (Geomagnetically Induced Currents, GICs) in conductive infrastructure (Boteler, 2021;Boteler et al., 1998;Rajput et al., 2020). Forecasting intervals of risk in a timely manner is key; this allows necessary mitigating action to be taken.

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Section: Introductionmentioning

confidence: 99%

On the Considerations of Using Near Real Time Data for Space Weather Hazard Forecasting

et al. 2022

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“…Smirnov et al (2020) predicted the electron flux for the medium energy (120-600 keV) based on satellite position, geomagnetic index and solar wind parameters (including velocity time history). Chu et al (2021) used a neural network (ANN) to build a relativistic electron model for the prediction of the Earth's outer radiation belt.…”

Section: Plain Language Summarymentioning

confidence: 99%

“…Chu et al. (2021) used a neural network (ANN) to build a relativistic electron model for the prediction of the Earth's outer radiation belt.…”

Section: Introductionmentioning

confidence: 99%

The Short‐Time Prediction of the Energetic Electron Flux in the Planetary Radiation Belt Based on Stacking Ensemble‐Learning Algorithm

Tang

Tao

et al. 2022

Space Weather

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Radiation belts have been observed at many magnetized planets in the solar system, such as Earth (Summers et al., 2009), Jupiter (Bolton et al., 2004) and Saturn (Carbary et al., 2009. Understanding the transport, acceleration and loss of radiation belt electrons is a major research topic in planetary magnetospheric physics (Tang & Summers, 2012;Tang et al., 2014) since these energetic electrons pose a potential hazard to orbiting satellites. Previously, the observational data being gathered by numerous satellites were incorporated into simulation models of the radiation belt (Ni et al., 2019), which remain widely used for spacecraft design purposes and space environment predictions. However, the practical space weather applications also require innovative approaches to the use of mass satellite data, and will lead to further advances in the research of planetary radiation belts (Ni et al., 2009).With the continuous development of artificial intelligence technology, scientists began to explore the applications of machine learning in space physics, such as the stellar atmospheric parameter (Ramirez et al., 2001),

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“…For solar activities, multiple deep‐learning models were fused to use in solar flare prediction (Tang, Liao, et al., 2021) and sunspot group classification (Tang, Zeng, et al., 2021). Multilayer Feedforward Neural Network (FNN) was also used in prediction of relativistic electrons in the outer radiation belt (Chu et al., 2021). And, Tang et al.…”

Section: Introductionmentioning

confidence: 99%

The Prediction of Storm‐Time Thermospheric Mass Density by LSTM‐Based Ensemble Learning

et al. 2022

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The accurate prediction of storm‐time thermospheric mass density is always critically important and also a challenge. In this paper, an available prediction model is established by Long Short‐Term Memory (LSTM)‐based ensemble learning algorithms. However, the generalization ability of the deep learning model is often suspicious since training data and testing data are from the same data set in the conventional method. Therefore, in order to objectively validate the performance and generalization of the model, we utilize the GOCE data for training and the SWARM‐C data for testing to verify its performance mainly during the geomagnetic storm period. The results show that the LSTM‐based ensemble learning model (LELM) is robust under different geomagnetic activity levels and has good generalization ability for the different satellite data set. The prediction accuracy of the LELM is proved to be better than a common‐used empirical model (NRLMSISE‐00). Thus, our approach provides a promising way to give reliable and stable predictions of thermospheric mass density.

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Relativistic Electron Model in the Outer Radiation Belt Using a Neural Network Approach

Cited by 44 publications

References 87 publications

On the Considerations of Using Near Real Time Data for Space Weather Hazard Forecasting

On the Considerations of Using Near Real Time Data for Space Weather Hazard Forecasting

The Short‐Time Prediction of the Energetic Electron Flux in the Planetary Radiation Belt Based on Stacking Ensemble‐Learning Algorithm

The Prediction of Storm‐Time Thermospheric Mass Density by LSTM‐Based Ensemble Learning

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