论文标题
为了耦合全盘和活跃的基于区域的耀斑预测，以进行操作空间预测
Towards Coupling Full-disk and Active Region-based Flare Prediction for Operational Space Weather Forecasting
论文作者
论文摘要
太阳火光预测是太空天气预报的核心问题，由于遥感以及机器学习和深度学习方法的最新进展，因此吸引了广泛研究人员的关注。基于机器和深度学习模型的实验发现揭示了针对特定任务数据集的显着改进性能。除了建筑模型外，在操作环境下将这种模型部署到生产环境中的实践是一个更复杂且通常是耗时的过程，通常在研究环境中直接解决。我们提供了一系列新的启发式方法，用于培训和部署$ \ geq $ M1.0级耀斑的操作太阳耀斑预测系统，并具有两种预测模式：全盘和基于活动的区域。在全盘模式下，使用深度学习模型在全盘视线图上进行预测，而在基于活动区域的模型中，使用多变量时间序列数据实例对每个活动区域发出预测。通过元模型将来自单个活动区域预测和全盘预测变量的输出组合到最终的全盘预测结果。我们利用了两个基础学习者的耀斑概率的同等加权平均合奏作为我们的基线元学习者，并通过训练逻辑回归模型来提高我们两个基本学习者的能力。这项研究的主要发现是：（i）我们成功耦合了两个使用不同数据集和模型体系结构训练的异质耀斑预测模型，以预测接下来的24小时的全盘耀斑概率，（ii）我们提出的结合模型，即，即逻辑回归，即在两个基础学习者和基础学习者中的预测性效果上提高了两种基础学习者的预测性能，并在两个方面进行了计算，以实现量的计算，以实现计算的计算。海德克技能核心（HSS）和（iii）我们的结果分析表明，基于逻辑回归的集合（Meta-fp）在全盘模型（基础学习者）上改善了$ \ sim9 \％$ n en tsss tss和$ \ sim10 \％$。同样，它在TSS和HSS方面分别在基于AR的模型（基本学习者）上改进了$ \ sim17 \％$和$ \ sim20 \％$。最后，与基线元模型相比，它在TSS上提高了$ \ sim10 \％$和HSS，而HSS则通过$ \ sim15 \％$提高。
Solar flare prediction is a central problem in space weather forecasting and has captivated the attention of a wide spectrum of researchers due to recent advances in both remote sensing as well as machine learning and deep learning approaches. The experimental findings based on both machine and deep learning models reveal significant performance improvements for task specific datasets. Along with building models, the practice of deploying such models to production environments under operational settings is a more complex and often time-consuming process which is often not addressed directly in research settings. We present a set of new heuristic approaches to train and deploy an operational solar flare prediction system for $\geq$M1.0-class flares with two prediction modes: full-disk and active region-based. In full-disk mode, predictions are performed on full-disk line-of-sight magnetograms using deep learning models whereas in active region-based models, predictions are issued for each active region individually using multivariate time series data instances. The outputs from individual active region forecasts and full-disk predictors are combined to a final full-disk prediction result with a meta-model. We utilized an equal weighted average ensemble of two base learners' flare probabilities as our baseline meta learner and improved the capabilities of our two base learners by training a logistic regression model. The major findings of this study are: (i) We successfully coupled two heterogeneous flare prediction models trained with different datasets and model architecture to predict a full-disk flare probability for next 24 hours, (ii) Our proposed ensembling model, i.e., logistic regression, improves on the predictive performance of two base learners and the baseline meta learner measured in terms of two widely used metrics True Skill Statistic (TSS) and Heidke Skill core (HSS), and (iii) Our result analysis suggests that the logistic regression-based ensemble (Meta-FP) improves on the full-disk model (base learner) by $\sim9\%$ in terms TSS and $\sim10\%$ in terms of HSS. Similarly, it improves on the AR-based model (base learner) by $\sim17\%$ and $\sim20\%$ in terms of TSS and HSS respectively. Finally, when compared to the baseline meta model, it improves on TSS by $\sim10\%$ and HSS by $\sim15\%$.
