Prediction of Solar Flares Using Photospheric Magnetic Field Parameters with Deep Learning

2026-06-20

AI Summary

This study addresses the limited interpretability and low trustworthiness of conventional deep learning models in solar flare prediction, which hinder their applicability in high-stakes decision-making. The authors propose a deep learning framework built upon photospheric magnetic field parameters and, for the first time, systematically integrate explainable artificial intelligence (XAI) techniques—specifically SHAP and partial dependence plots (PDP)—to provide both global and local interpretations of M- and X-class flare predictions. This approach elucidates the key predictive features and their underlying physical relationships, substantially enhancing model transparency and reliability. The resulting framework offers a novel, high-performance tool that balances predictive accuracy with interpretability, thereby advancing space weather forecasting and solar physics research.

Abstract

Solar flares, particularly those of the M- and X-class, have a significant impact on human life because of their potential to disrupt critical infrastructure and communication systems on Earth. Accurate prediction of solar flares is crucial for mitigating these risks, but the black-box nature of conventional deep learning models used in flare prediction limits their trustworthiness and interpretability. In this paper, we propose a new approach to solar flare prediction using photospheric magnetic field parameters or features with deep learning. To improve model interpretability, we integrate explainable artificial intelligence (XAI) techniques, including SHapley Additive exPlanations (SHAP) and partial dependence plots (PDPs), into our prediction framework. XAI methods provide transparency by analyzing the importance and interactions of features used by our model. Specifically, SHAP values offer a global and local understanding of the features, while PDPs provide insights into feature-level trends. These techniques demonstrate the potential of XAI in deploying AI-driven solutions in high-impact applications such as solar flare prediction, paving the way for more informed decision-making in solar physics and space weather studies.

Problem

• solar flares
• deep learning
• interpretability
• explainable AI
• space weather

Innovation

• Explainable AI
• SHAP
• Partial Dependence Plots
• Solar Flare Prediction
• Photospheric Magnetic Field