New Machine Learning Model Can Predict Radiation Storms

Researchers are now able to take preventive actions against energetic electrons through a two-day notice delivered by a sophisticated computer model.

A new machine learning computer system precisely foresees harmful radiation storms triggered by the Van Allen belts two days before the storm takes place. It is the most developed notice that currently exists, as per a new study published in the journal Space Weather.​

“Radiation storms from the Van Allen belts can damage or even knock out satellites orbiting in medium and high altitudes above the Earth, but predicting these storms has always been a challenge,” said Yue Chen, a space scientist at Los Alamos National Laboratory and principal investigator on the project funded by both NASA and NOAA.

“Given that the Van Allen Probes, which provided important data about space weather, recently de-orbited, we no longer have direct measurements about what’s happening in the outer electron radiation belt. Our new model uses existing data sets to ‘learn’ patterns and predict future storms so satellite operators can take protective measures, including temporarily shutting down part of or even the whole satellite to avoid damage,” Chen added.

This predictive system for megaelectron-volt (MeV) electrons featured inside Earth‘s outer Van Allen belt backs up a previous model that successfully foreseen radiation storms one day before the events would take place.

The Model Has More Applications

The new model, dubbed PreMevE 2.0, enhances predictions by including upstream solar wind velocities. It foresees future events by studying existing information series from NOAA and Los Alamos satellites to understand significant patterns of electron behavior.

“With the expectation that similar patterns may reveal themselves in the future, our model is capable of making predictions by capturing some critical signatures as a precursor to those future events,” explained Youzuo Lin, a computational scientist at Los Alamos who created the machine-learning algorithms for the model.

“By testing the model with multiple machine-learning algorithms, this work confirms the predictability of MeV electrons, as well as the robustness of using low-Earth-orbit electron observations to drive predictions,” added Chen. “In addition, the framework set up in this work allows us to easily include more input parameters to predict more energetic electrons in the next step.”

This machine learning structure created for PreMevE 2.0 can also be used in many other applications that utilize time-related calculations, such as predicting earthquake patterns among massive quantities of seismic time-series data, allowing for detection of small earthquakes from noisy conditions.

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