Advancing Solar Flare Forecasting with a Deep Learning Approach Using Multimodal Inputs

Abstract Solar flares are among the most energetic phenomena in the solar system, and their forecasting remains a major challenge in space weather research. In this study, we present a deep learning framework that predicts the occurrence of ≥C5.0-class flares within a 2 hr forecast horizon by integrating multimodal, multiwavelength observations of solar active regions. The model combines sequences of Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI) line-of-sight magnetograms, continuum intensity images, and SDO/Atmospheric Imaging Assembly extreme-ultraviolet observations (193 and 304 Å) with physically derived Space-weather HMI Active Region Patch parameters—total unsigned magnetic flux and current helicity—within a unified end-to-end architecture. A convolutional encoder extracts spatial representations from images, which are fused with physical parameters and processed through a recurrent neural network to capture temporal evolution. Using time sequences spanning 36 minutes, our best-performing configuration achieves an accuracy of 92.7%, a recall of 97.6%, and a true skill statistic of 0.86 on an independent test set, significantly outperforming single-modality baselines. Our findings demonstrate that combining physically meaningful parameters with multiwavelength imaging substantially enhances model performance while maintaining good calibration. The proposed framework demonstrates the capability of deep learning to model the spatiotemporal and physical complexity of solar active regions, thereby enabling more accurate and physics-informed flare forecasting.