NARX Neural Network Derivations of the Outer Boundary Radiation Belt Electron Flux

نویسندگان

چکیده

We present two new empirical models of radiation belt electron flux at geostationary orbit. GOES-15 measurements 0.8 MeV electrons were used to train a Nonlinear Autoregressive with Exogenous input (NARX) neural network for both modeling values and an upper boundary condition scaling factor (BF). The model utilizes feedback delay 2 time steps (i.e., 5 min steps) the most efficient number hidden layers set 10. Magnetic local time, Dst, Kp, solar wind dynamic pressure, AE, velocity found perform as predicative indicators therefore exogenous inputs. NARX-derived was in conjunction Versatile Electron Radiation Belt (VERB) code produce reconstructions belts during period July–November 1990, independent in-situ observations. Here, Kp chosen sole be more compatible VERB code. This Combined Release Effects Satellite-era reconstruction showcases potential use these network-derived conditions method hindcasting historical belts. study serves companion paper another recently published on reconstructing Solar Cycles 17–24 (Saikin et al., 2021, https://doi.org/10.1029/2020sw002524), which results featured this used.

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ژورنال

عنوان ژورنال: Space Weather-the International Journal of Research and Applications

سال: 2022

ISSN: ['1542-7390']

DOI: https://doi.org/10.1029/2021sw002774