Prediction of radiation belts electron fluxes at a Low Earth Orbit using neural networks with PROBA-V/EPT data
| dc.contributor.author | Botek, E. | |
| dc.contributor.author | Pierrard, V. | |
| dc.contributor.author | Winant, A. | |
| dc.date | 2023 | |
| dc.date.accessioned | 2023-08-10T09:22:30Z | |
| dc.date.available | 2023-08-10T09:22:30Z | |
| dc.identifier.uri | https://orfeo.belnet.be/handle/internal/11048 | |
| dc.description | We introduce for the first time the PROBA-V/EPT electron flux data to train a deep learning data-driven model with the purpose of investigating the Earth’s radiation belts dynamics. The Long-Short Term Memory Neural Network is employed to predict the electron fluxes between 1 and 8 Earth Radius (RE) along a Low Earth Orbit. Different combinations of time series inputs involving Solar Wind and geomagnetic data are tested, based on previous knowledge of their impact onto the high energy radiation fluxes. Two Energetic Particle Telescope energy channels feed the learning procedure for nonrelativistic (0.5–0.6 MeV) and relativistic (1.0–2.4 MeV) electron fluxes. A good performance of the model employing different time resolutions from hours to days is demonstrated with a correlation of more than 0.9 between the predicted and out-of-sample fluxes, and a prediction efficiency that can attain between 0.6 and 0.9 depending on the L range. The analysis of different input parameters and time resolutions allows to construct the best data set structure and improve the model to identify relevant effects such as dropouts, flux increase and recovery features. | |
| dc.language | eng | |
| dc.title | Prediction of radiation belts electron fluxes at a Low Earth Orbit using neural networks with PROBA-V/EPT data | |
| dc.type | Article | |
| dc.subject.frascati | Physical sciences | |
| dc.audience | Scientific | |
| dc.subject.free | radiation belts | |
| dc.subject.free | PROBA-V/EPT | |
| dc.subject.free | electron fluxes | |
| dc.subject.free | neural networks | |
| dc.subject.free | forecast | |
| dc.source.title | Space Weather | |
| dc.source.volume | 21 | |
| dc.source.issue | 7 | |
| dc.source.page | e2023SW003466 | |
| Orfeo.peerreviewed | Yes | |
| dc.identifier.doi | 10.1029/2023SW003466 | |
| dc.identifier.scopus |
