Abstract
Although space weather events may not directly affect human life, they have the potential to inflict significant harm upon our communities. Harmful space weather events can trigger atmospheric changes that result in physical and economic damages on a global scale. In 1989, Earth experienced the effects of a powerful geomagnetic storm that caused satellites to malfunction, while triggering power blackouts in Canada, along with electricity disturbances in the United States and Europe. With the solar cycle peak rapidly approaching, there is an ever-increasing need to prepare and prevent the damages that can occur, especially to modern-day technology, calling for the need of a comprehensive prediction system. This study aims to leverage machine learning techniques to predict instances of space weather (solar flares, coronal mass ejections, geomagnetic storms), based on active region magnetograms of the Sun. This was done through the use of the NASA DONKI service to determine when these solar events occur, then using data from the NASA Solar Dynamics Observatory to compile a dataset that includes magnetograms of active regions of the Sun 24 hours before the events. By inputting the magnetograms into a convolutional neural network (CNN) trained from this dataset, it can serve to predict whether a space weather event will occur, and what type of event it will be. The model was designed using a custom architecture CNN, and returned an accuracy of 90.27%, a precision of 85.83%, a recall of 91.78%, and an average F1 score of 92.14% across each class (Solar flare [Flare], geomagnetic storm [GMS], coronal mass ejection [CME]). Our results show that using magnetogram data as an input for a CNN is a viable method to space weather prediction. Future work can involve prediction of the magnitude of solar events.
Abstract (translated)
尽管空间天气事件可能不会直接影响人类生活，但它们有可能对社区造成严重伤害。有害的空间天气事件可能引发全球性的大气变化，导致物理和经济损失。1989年，地球经历了强大的地磁暴，导致卫星故障，引发了加拿大的电力停电以及美国和欧洲的电力干扰。随着太阳周期的临近，越来越多地需要为预防可能出现的损坏做好准备，特别是对现代技术，这就要求建立一个全面预测系统。 本研究旨在利用机器学习技术，根据太阳活动区磁图预测空间天气事件（太阳黑子活动，日冕物质抛射，地磁暴）。这是通过使用NASA的DONKI服务确定这些太阳事件发生的时间，然后使用NASA的Solar Dynamics Observatory收集这些事件发生前24小时的磁图数据。将磁图输入到从该数据集中提取的卷积神经网络（CNN）中，可以预测是否会发生空间天气事件以及事件类型。该模型采用了一种自定义的CNN架构，在每类上取得了90.27%的准确度、85.83%的精度、91.78%的召回率和92.14%的均方误差（F1）分数。我们的结果表明，使用磁图数据作为CNN的输入是一种可行的空间天气预测方法。未来的工作可以包括预测太阳事件的规模。
URL
https://arxiv.org/abs/2405.02545
