by Rafael Pires de Lima and Yue Chen
Relativistic electrons in Earth's outer radiation belt reach megaelectron-volt (MeV) energy and pose a severe space weather threat for spaceborne electronics orbiting our planet.
Ideally, stakeholders such as the space industry, service providers, and government agencies, count on reliable model forecasts of MeV electrons to take necessary mitigation measures to protect their space assets.
In the following notebook, we show case how flux distributions of one MeV electrons can be predicted by the PreMevE model using a machine learning approach. Here we rely on scikit-learn and TensorFlow packages for model algorithim implementation.
The content we show is part of a bigger analysis published in the Forecasting Megaelectron‐Volt Electrons Inside Earth's Outer Radiation Belt: PreMevE 2.0 Based on Supervised Machine Learning Algorithms paper.