Embedding Ordinality to Binary Loss Function for Improving Solar Flare Forecasting

• URL: http://arxiv.org/abs/2408.11768v1
• Date: Wed, 21 Aug 2024 16:42:58 GMT
• Title: Embedding Ordinality to Binary Loss Function for Improving Solar Flare Forecasting
• Authors: Chetraj Pandey, Anli Ji, Jinsu Hong, Rafal A. Angryk, Berkay Aydin,
• Abstract summary: We introduce a novel approach to encode ordinality into a binary loss function showing an application to solar flare prediction. We enhance solar flare forecasting by enabling flare predictions for each AR across the entire solar disk, without any longitudinal restrictions.
• Score: 1.0225653612678713
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we propose a novel loss function aimed at optimizing the binary flare prediction problem by embedding the intrinsic ordinal flare characteristics into the binary cross-entropy (BCE) loss function. This modification is intended to provide the model with better guidance based on the ordinal characteristics of the data and improve the overall performance of the models. For our experiments, we employ a ResNet34-based model with transfer learning to predict $\geq$M-class flares by utilizing the shape-based features of magnetograms of active region (AR) patches spanning from $-$90$^{\circ}$ to $+$90$^{\circ}$ of solar longitude as our input data. We use a composite skill score (CSS) as our evaluation metric, which is calculated as the geometric mean of the True Skill Score (TSS) and the Heidke Skill Score (HSS) to rank and compare our models' performance. The primary contributions of this work are as follows: (i) We introduce a novel approach to encode ordinality into a binary loss function showing an application to solar flare prediction, (ii) We enhance solar flare forecasting by enabling flare predictions for each AR across the entire solar disk, without any longitudinal restrictions, and evaluate and compare performance. (iii) Our candidate model, optimized with the proposed loss function, shows an improvement of $\sim$7%, $\sim$4%, and $\sim$3% for AR patches within $\pm$30$^\circ$, $\pm$60$^\circ$, and $\pm$90$^\circ$ of solar longitude, respectively in terms of CSS, when compared with standard BCE. Additionally, we demonstrate the ability to issue flare forecasts for ARs in near-limb regions (regions between $\pm$60$^{\circ}$ to $\pm$90$^{\circ}$) with a CSS=0.34 (TSS=0.50 and HSS=0.23), expanding the scope of AR-based models for solar flare prediction. This advances the reliability of solar flare forecasts, leading to more effective prediction capabilities.

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)
• Advancing Solar Flare Prediction using Deep Learning with Active Region Patches [1.0225653612678713]
  We introduce a novel methodology for leveraging shape-based characteristics of magnetograms of active region (AR) patches. We create three deep learning models: (i) ResNet34, (ii) MobileNet, and (iii) MobileViT to predict flares and assess their efficacy.
  arXiv  Detail & Related papers  (2024-06-16T19:35:41Z)
• 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)
• A Meta-Learning Approach to Predicting Performance and Data Requirements [163.4412093478316]
  We propose an approach to estimate the number of samples required for a model to reach a target performance. We find that the power law, the de facto principle to estimate model performance, leads to large error when using a small dataset. We introduce a novel piecewise power law (PPL) that handles the two data differently.
  arXiv  Detail & Related papers  (2023-03-02T21:48:22Z)
• Autoregressive Bandits [58.46584210388307]
  We propose a novel online learning setting, Autoregressive Bandits, in which the observed reward is governed by an autoregressive process of order $k$. We show that, under mild assumptions on the reward process, the optimal policy can be conveniently computed. We then devise a new optimistic regret minimization algorithm, namely, AutoRegressive Upper Confidence Bound (AR-UCB), that suffers sublinear regret of order $widetildemathcalO left( frac(k+1)3/2sqrtnT (1-G
  arXiv  Detail & Related papers  (2022-12-12T21:37:36Z)
• 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)
• Provably Efficient Offline Reinforcement Learning with Trajectory-Wise Reward [66.81579829897392]
  We propose a novel offline reinforcement learning algorithm called Pessimistic vAlue iteRaTion with rEward Decomposition (PARTED) PARTED decomposes the trajectory return into per-step proxy rewards via least-squares-based reward redistribution, and then performs pessimistic value based on the learned proxy reward. To the best of our knowledge, PARTED is the first offline RL algorithm that is provably efficient in general MDP with trajectory-wise reward.
  arXiv  Detail & Related papers  (2022-06-13T19:11:22Z)
• Human-in-the-loop: Provably Efficient Preference-based Reinforcement Learning with General Function Approximation [107.54516740713969]
  We study human-in-the-loop reinforcement learning (RL) with trajectory preferences. Instead of receiving a numeric reward at each step, the agent only receives preferences over trajectory pairs from a human overseer. We propose the first optimistic model-based algorithm for PbRL with general function approximation.
  arXiv  Detail & Related papers  (2022-05-23T09:03:24Z)
• Regret-Optimal Filtering [57.51328978669528]
  We consider the problem of filtering in linear state-space models through the lens of regret optimization. We formulate a novel criterion for filter design based on the concept of regret between the estimation error energy of a clairvoyant estimator. We show that the regret-optimal estimator can be easily implemented by solving three Riccati equations and a single Lyapunov equation.
  arXiv  Detail & Related papers  (2021-01-25T19:06:52Z)

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.