An Interpretable Transformer Model for Operational Flare Forecasting

Abstract

Interpretable machine learning tools including LIME (Local Interpretable Model-agnostic Explanations) and ALE (Accumulated Local Effects) are incorporated into a transformer-based deep learning model, named SolarFlareNet, to interpret the predictions made by the model. SolarFlareNet is implemented into an operational flare forecasting system to predict whether an active region on the surface of the Sun would produce a >=M class flare within the next 24 hours. LIME determines the ranking of the features used by SolarFlareNet. 2D ALE plots identify the interaction effects of two features on the predictions. Together, these tools help scientists better understand which features are crucial for SolarFlareNet to make its predictions. Experiments show that the tools can explain the internal workings of SolarFlareNet while maintaining its accuracy.