Unveiling the Potential of Deep Learning Models for Solar Flare Prediction in Near-Limb Regions

• URL: http://arxiv.org/abs/2309.14483v1
• Date: Mon, 25 Sep 2023 19:30:02 GMT
• Title: Unveiling the Potential of Deep Learning Models for Solar Flare Prediction in Near-Limb Regions
• Authors: Chetraj Pandey, Rafal A. Angryk, Berkay Aydin
• Abstract summary: This study aims to evaluate the performance of deep learning models in predicting $geq$M-class solar flares with a prediction window of 24 hours. We trained three well-known deep learning architectures--AlexNet, VGG16, and ResNet34 using transfer learning. Our research findings demonstrate that our models are capable of discerning complex spatial patterns from full-disk magnetograms.
• Score: 1.2699007098398802
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study aims to evaluate the performance of deep learning models in predicting $\geq$M-class solar flares with a prediction window of 24 hours, using hourly sampled full-disk line-of-sight (LoS) magnetogram images, particularly focusing on the often overlooked flare events corresponding to the near-limb regions (beyond $\pm$70$^{\circ}$ of the solar disk). We trained three well-known deep learning architectures--AlexNet, VGG16, and ResNet34 using transfer learning and compared and evaluated the overall performance of our models using true skill statistics (TSS) and Heidke skill score (HSS) and computed recall scores to understand the prediction sensitivity in central and near-limb regions for both X- and M-class flares. The following points summarize the key findings of our study: (1) The highest overall performance was observed with the AlexNet-based model, which achieved an average TSS$\sim$0.53 and HSS$\sim$0.37; (2) Further, a spatial analysis of recall scores disclosed that for the near-limb events, the VGG16- and ResNet34-based models exhibited superior prediction sensitivity. The best results, however, were seen with the ResNet34-based model for the near-limb flares, where the average recall was approximately 0.59 (the recall for X- and M-class was 0.81 and 0.56 respectively) and (3) Our research findings demonstrate that our models are capable of discerning complex spatial patterns from full-disk magnetograms and exhibit skill in predicting solar flares, even in the vicinity of near-limb regions. This ability holds substantial importance for operational flare forecasting systems.

Related papers

• OPUS: Occupancy Prediction Using a Sparse Set [64.60854562502523]
  We present a framework to simultaneously predict occupied locations and classes using a set of learnable queries. OPUS incorporates a suite of non-trivial strategies to enhance model performance. Our lightest model achieves superior RayIoU on the Occ3D-nuScenes dataset at near 2x FPS, while our heaviest model surpasses previous best results by 6.1 RayIoU.
  arXiv  Detail & Related papers  (2024-09-14T07:44:22Z)
• Optimization Efficient Open-World Visual Region Recognition [55.76437190434433]
  RegionSpot integrates position-aware localization knowledge from a localization foundation model with semantic information from a ViL model. Experiments in open-world object recognition show that our RegionSpot achieves significant performance gain over prior alternatives.
  arXiv  Detail & Related papers  (2023-11-02T16:31:49Z)
• Towards Interpretable Solar Flare Prediction with Attention-based Deep Neural Networks [1.1624569521079424]
  Solar flare prediction is a central problem in space weather forecasting. We developed an attention-based deep learning model to perform full-disk binary flare predictions. Our model can learn conspicuous features corresponding to active regions from full-disk magnetogram images.
  arXiv  Detail & Related papers  (2023-09-08T19:21:10Z)
• Exploring Deep Learning for Full-disk Solar Flare Prediction with Empirical Insights from Guided Grad-CAM Explanations [4.085931783551287]
  This study progresses solar flare prediction research by presenting a full-disk deep-learning model to forecast $geq$M-class solar flares. Our analysis unveils that full-disk solar flare predictions correspond with active region characteristics.
  arXiv  Detail & Related papers  (2023-08-30T02:24:09Z)
• Explainable Deep Learning-based Solar Flare Prediction with post hoc Attention for Operational Forecasting [0.6299766708197884]
  This paper presents a post hoc analysis of a deep learning-based full-disk solar flare prediction model. We used hourly full-disk line-of-sight magnetogram images and selected binary prediction mode to predict the occurrence of flares within 24 hours. Our analysis shows that full-disk predictions of solar flares align with characteristics related to the active regions.
  arXiv  Detail & Related papers  (2023-08-04T19:33:25Z)
• Explaining Full-disk Deep Learning Model for Solar Flare Prediction using Attribution Methods [0.6882042556551611]
  We present a solar flare prediction model, which is trained using hourly full-disk line-of-sight magnetogram images. We evaluate the overall performance of our model using the true skill statistic (TSS) and Heidke skill score (HSS) Our analysis revealed that full-disk prediction of solar flares aligns with characteristics related to active regions (ARs)
  arXiv  Detail & Related papers  (2023-07-29T03:18:56Z)
• Deep Learning for Day Forecasts from Sparse Observations [60.041805328514876]
  Deep neural networks offer an alternative paradigm for modeling weather conditions. MetNet-3 learns from both dense and sparse data sensors and makes predictions up to 24 hours ahead for precipitation, wind, temperature and dew point. MetNet-3 has a high temporal and spatial resolution, respectively, up to 2 minutes and 1 km as well as a low operational latency.
  arXiv  Detail & Related papers  (2023-06-06T07:07:54Z)
• Predictive World Models from Real-World Partial Observations [66.80340484148931]
  We present a framework for learning a probabilistic predictive world model for real-world road environments. While prior methods require complete states as ground truth for learning, we present a novel sequential training method to allow HVAEs to learn to predict complete states from partially observed states only.
  arXiv  Detail & Related papers  (2023-01-12T02:07:26Z)
• Towards Coupling Full-disk and Active Region-based Flare Prediction for Operational Space Weather Forecasting [0.5872014229110215]
  We present new approaches to train and deploy an operational solar flare prediction system for $geq$M1.0-class flares. In full-disk mode, predictions are performed on full-disk line-of-sight magnetograms using deep learning models. In active region-based models, predictions are issued for each active region individually.
  arXiv  Detail & Related papers  (2022-08-11T22:34:44Z)
• Strict baselines for Covid-19 forecasting and ML perspective for USA and Russia [105.54048699217668]
  Covid-19 allows researchers to gather datasets accumulated over 2 years and to use them in predictive analysis. We present the results of a consistent comparative study of different types of methods for predicting the dynamics of the spread of Covid-19 based on regional data for two countries: the United States and Russia.
  arXiv  Detail & Related papers  (2022-07-15T18:21:36Z)
• From Sound Representation to Model Robustness [82.21746840893658]
  We investigate the impact of different standard environmental sound representations (spectrograms) on the recognition performance and adversarial attack robustness of a victim residual convolutional neural network. Averaged over various experiments on three environmental sound datasets, we found the ResNet-18 model outperforms other deep learning architectures.
  arXiv  Detail & Related papers  (2020-07-27T17:30:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.