Explainable AI in Deep Learning-Based Prediction of Solar Storms

TL;DR

This paper introduces an interpretable deep learning model using LSTM with attention for predicting solar storms, enhancing transparency and understanding of the model's decision-making process in solar flare and CME prediction.

Contribution

It presents the first interpretable LSTM-based model for solar storm prediction, combining time series modeling with post hoc interpretability techniques.

Findings

Model achieves accurate solar storm predictions.

Interpretability techniques reveal key factors influencing predictions.

Provides insights into the temporal dynamics of solar active regions.

Abstract

A deep learning model is often considered a black-box model, as its internal workings tend to be opaque to the user. Because of the lack of transparency, it is challenging to understand the reasoning behind the model's predictions. Here, we present an approach to making a deep learning-based solar storm prediction model interpretable, where solar storms include solar flares and coronal mass ejections (CMEs). This deep learning model, built based on a long short-term memory (LSTM) network with an attention mechanism, aims to predict whether an active region (AR) on the Sun's surface that produces a flare within 24 hours will also produce a CME associated with the flare. The crux of our approach is to model data samples in an AR as time series and use the LSTM network to capture the temporal dynamics of the data samples. To make the model's predictions accountable and reliable, we leverage post hoc model-agnostic techniques, which help elucidate the factors contributing to the predicted output for an input sequence and provide insights into the model's behavior across multiple sequences within an AR. To our knowledge, this is the first time that interpretability has been added to an LSTM-based solar storm prediction model.