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.

利用机器学习技术，通过太阳活动区磁谱图，预测空间天气事件（太阳耀斑、地磁风暴、日冕物质抛射），建立了具有 90.27% 准确度、85.83% 精确度、91.78% 召回率和 92.14% 平均 F1 分数的卷积神经网络模型，证明磁谱图数据对于空间天气预测具有可行性。

利用卷积神经网络分析活动区磁图预测空间天气事件

Prediction of Space Weather Events through Analysis of Active Region  Magnetograms using Convolutional Neural Network

The detection and analysis of the solar coronal holes (CHs) is an important
field of study in the domain of solar physics. Mainly, it is required for the
proper prediction of the geomagnetic storms which directly or indirectly affect
various space and ground-based systems. For the detection of CHs till date, the
solar scientist depends on manual hand-drawn approaches. However, with the
advancement of image processing technologies, some automated image segmentation
methods have been used for the detection of CHs. In-spite of this, fast and
accurate detection of CHs are till a major issues. Here in this work, a novel
quantum computing-based fast fuzzy c-mean technique has been developed for fast
detection of the CHs region. The task has been carried out in two stages, in
first stage the solar image has been segmented using a quantum computing based
fast fuzzy c-mean (QCFFCM) and in the later stage the CHs has been extracted
out from the segmented image based on image morphological operation. In the
work, quantum computing has been used to optimize the cost function of the fast
fuzzy c-mean (FFCM) algorithm, where quantum approximate optimization algorithm
(QAOA) has been used to optimize the quadratic part of the cost function. The
proposed method has been tested for 193 \AA{} SDO/AIA full-disk solar image
datasets and has been compared with the existing techniques. The outcome shows
the comparable performance of the proposed method with the existing one within
a very lesser time.

本研究提出了一种基于量子计算的快速模糊 C 均值方法来快速检测太阳冠空洞，通过图像处理和形态学运算从分割图像中提取冠空洞区域，与现有技术相比，在较短时间内展现了可比较的性能。

一种基于量子模糊的实时太阳冕孔检测方法

A Quantum Fuzzy-based Approach for Real-Time Detection of Solar Coronal  Holes

Geomagnetic storms (GS) occur when solar winds disrupt Earth's magnetosphere.
GS can cause severe damages to satellites, power grids, and communication
infrastructures. Estimate of direct economic impacts of a large scale GS
exceeds $40 billion a day in the US. Early prediction is critical in preventing
and minimizing the hazards. However, current methods either predict several
hours ahead but fail to identify all types of GS, or make predictions within
short time, e.g., one hour ahead of the occurrence. This work aims to predict
all types of geomagnetic storms reliably and as early as possible using big
data and machine learning algorithms. By fusing big data collected from
multiple ground stations in the world on different aspects of solar
measurements and using Random Forests regression with feature selection and
downsampling on minor geomagnetic storm instances (which carry majority of the
data), we are able to achieve an accuracy of 82.55% on data collected in 2021
when making early predictions three hours in advance. Given that important
predictive features such as historic Kp indices are measured every 3 hours and
their importance decay quickly with the amount of time in advance, an early
prediction of 3 hours ahead of time is believed to be close to the practical
limit.

使用大数据和机器学习算法，通过融合世界各地多个地面站收集到的关于太阳测量不同方面的大数据，并使用具有特征选择和降采样功能的随机森林回归方法来对次要地磁风暴实例进行预测，我们能够在提前三小时进行早期预测时，在 2021 年收集的数据上实现 82.55% 的准确率。考虑到重要的预测特征，例如历史的 Kp 指数每隔 3 小时测量一次，并且随着提前时间的增加，它们的重要性迅速衰减，预测提前 3 小时被认为接近实际限制。