NARX Neural Network Derivations of the Outer Boundary Radiation Belt Electron Flux
Abstract We present two new empirical models of radiation belt electron flux at geostationary orbit. GOES‐15 measurements of 0.8 MeV electrons were used to train a Nonlinear Autoregressive with Exogenous input (NARX) neural network for both modeling GOES‐15 flux values and an upper boundary conditio...
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Wiley
2022-05-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2021SW002774 |
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| author | D. A. Landis A. A. Saikin I. Zhelavskaya A. Y. Drozdov N. Aseev Y. Y. Shprits M. F. Pfitzer A. G. Smirnov |
| author_facet | D. A. Landis A. A. Saikin I. Zhelavskaya A. Y. Drozdov N. Aseev Y. Y. Shprits M. F. Pfitzer A. G. Smirnov |
| author_sort | D. A. Landis |
| collection | DOAJ |
| description | Abstract We present two new empirical models of radiation belt electron flux at geostationary orbit. GOES‐15 measurements of 0.8 MeV electrons were used to train a Nonlinear Autoregressive with Exogenous input (NARX) neural network for both modeling GOES‐15 flux values and an upper boundary condition scaling factor (BF). The GOES‐15 flux model utilizes an input and feedback delay of 2 and 2 time steps (i.e., 5 min time steps) with the most efficient number of hidden layers set to 10. Magnetic local time, Dst, Kp, solar wind dynamic pressure, AE, and solar wind velocity were found to perform as predicative indicators of GOES‐15 flux and therefore were used as the exogenous inputs. The NARX‐derived upper boundary condition scaling factor was used in conjunction with the Versatile Electron Radiation Belt (VERB) code to produce reconstructions of the radiation belts during the period of July–November 1990, independent of in‐situ observations. Here, Kp was chosen as the sole exogenous input to be more compatible with the VERB code. This Combined Release and Radiation Effects Satellite‐era reconstruction showcases the potential to use these neural network‐derived boundary conditions as a method of hindcasting the historical radiation belts. This study serves as a companion paper to another recently published study on reconstructing the radiation belts during Solar Cycles 17–24 (Saikin et al., 2021, https://doi.org/10.1029/2020sw002524), for which the results featured in this paper were used. |
| format | Article |
| id | doaj-art-aff6a16d278e456c9211e2258d1f177b |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2022-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-aff6a16d278e456c9211e2258d1f177b2025-01-14T16:31:05ZengWileySpace Weather1542-73902022-05-01205n/an/a10.1029/2021SW002774NARX Neural Network Derivations of the Outer Boundary Radiation Belt Electron FluxD. A. Landis0A. A. Saikin1I. Zhelavskaya2A. Y. Drozdov3N. Aseev4Y. Y. Shprits5M. F. Pfitzer6A. G. Smirnov7Department of Earth, Planetary, and Space Sciences University of California Los Angeles CA USADepartment of Earth, Planetary, and Space Sciences University of California Los Angeles CA USAHelmoltz Centre Potsdam‐ GFZ German Research Centre for Geosciences Potsdam GermanyDepartment of Earth, Planetary, and Space Sciences University of California Los Angeles CA USAHelmoltz Centre Potsdam‐ GFZ German Research Centre for Geosciences Potsdam GermanyDepartment of Earth, Planetary, and Space Sciences University of California Los Angeles CA USAHelmoltz Centre Potsdam‐ GFZ German Research Centre for Geosciences Potsdam GermanyHelmoltz Centre Potsdam‐ GFZ German Research Centre for Geosciences Potsdam GermanyAbstract We present two new empirical models of radiation belt electron flux at geostationary orbit. GOES‐15 measurements of 0.8 MeV electrons were used to train a Nonlinear Autoregressive with Exogenous input (NARX) neural network for both modeling GOES‐15 flux values and an upper boundary condition scaling factor (BF). The GOES‐15 flux model utilizes an input and feedback delay of 2 and 2 time steps (i.e., 5 min time steps) with the most efficient number of hidden layers set to 10. Magnetic local time, Dst, Kp, solar wind dynamic pressure, AE, and solar wind velocity were found to perform as predicative indicators of GOES‐15 flux and therefore were used as the exogenous inputs. The NARX‐derived upper boundary condition scaling factor was used in conjunction with the Versatile Electron Radiation Belt (VERB) code to produce reconstructions of the radiation belts during the period of July–November 1990, independent of in‐situ observations. Here, Kp was chosen as the sole exogenous input to be more compatible with the VERB code. This Combined Release and Radiation Effects Satellite‐era reconstruction showcases the potential to use these neural network‐derived boundary conditions as a method of hindcasting the historical radiation belts. This study serves as a companion paper to another recently published study on reconstructing the radiation belts during Solar Cycles 17–24 (Saikin et al., 2021, https://doi.org/10.1029/2020sw002524), for which the results featured in this paper were used.https://doi.org/10.1029/2021SW002774radiation beltsforecasting (1922, 4315, 7924, 7964)machine learning (0555) |
| spellingShingle | D. A. Landis A. A. Saikin I. Zhelavskaya A. Y. Drozdov N. Aseev Y. Y. Shprits M. F. Pfitzer A. G. Smirnov NARX Neural Network Derivations of the Outer Boundary Radiation Belt Electron Flux Space Weather radiation belts forecasting (1922, 4315, 7924, 7964) machine learning (0555) |
| title | NARX Neural Network Derivations of the Outer Boundary Radiation Belt Electron Flux |
| title_full | NARX Neural Network Derivations of the Outer Boundary Radiation Belt Electron Flux |
| title_fullStr | NARX Neural Network Derivations of the Outer Boundary Radiation Belt Electron Flux |
| title_full_unstemmed | NARX Neural Network Derivations of the Outer Boundary Radiation Belt Electron Flux |
| title_short | NARX Neural Network Derivations of the Outer Boundary Radiation Belt Electron Flux |
| title_sort | narx neural network derivations of the outer boundary radiation belt electron flux |
| topic | radiation belts forecasting (1922, 4315, 7924, 7964) machine learning (0555) |
| url | https://doi.org/10.1029/2021SW002774 |
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