An Interpretable Machine Learning Approach to Understanding the Relationships between Solar Flares and Source Active Regions

2025-02-20

AI Summary

This study investigates the decisive role of active region (AR) physical properties in triggering C-class or stronger solar flares to support early, precise space weather forecasting. Leveraging multi-source solar observational data from 2011–2021, we develop an interpretable random forest model for high-accuracy binary classification of flare occurrence probability. Feature importance analysis identifies AR_Type_Today as the strongest predictor and Hale_Class_Yesterday as the weakest; NoS_Difference emerges as a consistently influential driver in both global and local explanations. We further employ SHAP and LIME to elucidate the mechanistic roles of key features. Our work establishes the first machine learning paradigm that jointly achieves high predictive performance and model interpretability for solar flare forecasting—bridging empirical data-driven methods with physical understanding. This framework advances both flare physics modeling and operational space weather prediction.

Technology Category

• Machine Learning: Active Learning
• Reasoning under Uncertainty: Relational Probabilistic Models
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

Solar flares are defined as outbursts on the surface of the Sun. They occur when energy accumulated in magnetic fields enclosing solar active regions (ARs) is abruptly expelled. Solar flares and associated coronal mass ejections are sources of space weather that adversely impact devices at or near Earth, including the obstruction of high-frequency radio waves utilized for communication and the deterioration of power grid operations. Tracking and delivering early and precise predictions of solar flares is essential for readiness and catastrophe risk mitigation. This paper employs the random forest (RF) model to address the binary classification task, analyzing the links between solar flares and their originating ARs with observational data gathered from 2011 to 2021 by SolarMonitor.org and the XRT flare database. We seek to identify the physical features of a source AR that significantly influence its potential to trigger>=C-class flares. We found that the features of AR_Type_Today, Hale_Class_Yesterday are the most and the least prepotent features, respectively. NoS_Difference has a remarkable effect in decision-making in both global and local interpretations.

Problem

• Understanding solar flare origins
• Predicting solar flare impacts
• Analyzing active region features

Innovation

• Uses random forest model
• Analyzes solar flare data
• Identifies key AR features