# Advancing Solar Flare Prediction using Deep Learning with Active Region Patches

arXiv: https://arxiv.org/abs/2406.11054 · HTML: https://mlsanity.com/paper/2406.11054/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 34 references that are in the map.
- First posted on arXiv 16 Jun 2024.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://mlsanity.com/paper/2110.02178/) (2021)
- [Towards Coupling Full-disk and Active Region-based Flare Prediction for Operational Space Weather Forecasting](https://mlsanity.com/paper/2209.07406/) (2022)
- [Explaining Full-disk Deep Learning Model for Solar Flare Prediction using Attribution Methods](https://mlsanity.com/paper/2307.15878/) (2023)
- [Towards Interpretable Solar Flare Prediction with Attention-based Deep Neural Networks](https://mlsanity.com/paper/2309.04558/) (2023)
- [Exploring Deep Learning for Full-disk Solar Flare Prediction with Empirical Insights from Guided Grad-CAM Explanations](https://mlsanity.com/paper/2308.15712/) (2023)
- [Unveiling the Potential of Deep Learning Models for Solar Flare Prediction in Near-Limb Regions](https://mlsanity.com/paper/2309.14483/) (2023)

## Built on by (most cited first, 4 of 4)

- [Embedding Ordinality to Binary Loss Function for Improving Solar Flare Forecasting](https://mlsanity.com/paper/2408.11768/) (2024)
- [SuryaBench: Benchmark Dataset for Advancing Machine Learning in Heliophysics and Space Weather Prediction](https://mlsanity.com/paper/2508.14107/) (2025)
- [Ordinal Encoding as a Regularizer in Binary Loss for Solar Flare Prediction](https://mlsanity.com/paper/2510.04063/) (2025)
- [Contrastive Heliophysical Image Pretraining for Solar Dynamics Observatory Records](https://mlsanity.com/paper/2511.22958/) (2025)

## Abstract (arXiv)

In this paper, we introduce a novel methodology for leveraging shape-based characteristics of magnetograms of active region (AR) patches and provide a novel capability for predicting solar flares covering the entirety of the solar disk (AR patches spanning from -90circ to +90circ of solar longitude). We create three deep learning models: (i) ResNet34, (ii) MobileNet, and (iii) MobileViT to predict ≥M-class flares and assess the efficacy of these models across various ranges of solar longitude. Given the inherent imbalance in our data, we employ augmentation techniques alongside undersampling during the model training phase, while maintaining imbalanced partitions in the testing data for realistic evaluation. We use a composite skill score (CSS) as our evaluation metric, computed as the geometric mean of the True Skill Score (TSS) and the Heidke Skill Score (HSS) to rank and compare models. The primary contributions of this work are as follows: (i) We introduce a novel capability in solar flare prediction that allows predicting flares for each ARs throughout the solar disk and evaluate and compare the performance, (ii) Our candidate model (MobileNet) achieves a CSS=0.51 (TSS=0.60 and HSS=0.44), CSS=0.51 (TSS=0.59 and HSS=0.44), and CSS=0.48 (TSS=0.56 and HSS=0.40) for AR patches within ±30circ, ±60circ, ±90circ of solar longitude respectively. Additionally, we demonstrate the ability to issue flare forecasts for ARs in near-limb regions (regions between ±60circ to ±90 circ) with a CSS=0.39 (TSS=0.48 and HSS=0.32), expanding the scope of AR-based models for solar flare prediction. This advancement opens new avenues for more reliable prediction of solar flares, thereby contributing to improved forecasting capabilities.