  The sun is highly complex in nature and its observatory imagery features is
one of the most important sources of information about the sun activity, space
and Earth's weather conditions. The NASA, solar Dynamics Observatory captures
approximately 70,000 images of the sun activity in a day and the continuous
visual inspection of this solar observatory images is challenging. In this
study, we developed a technique of tracking the sun's activity using 2D
circular kernel time series transformation, statistical and entropy measures,
with machine learning approaches. The technique involves transforming the solar
observatory image section into 1-Dimensional time series (1-DTS) while the
statistical and entropy measures (Approach 1) and direct classification
(Approach 2) is used to capture the extraction features from the 1-DTS for
machine learning classification into 'solar storm' and 'no storm'. We found
that the potential accuracy of the model in tracking the activity of the sun is
approximately 0.981 for Approach 1 and 0.999 for Approach 2. The stability of
the developed approach to rotational transformation of the solar observatory
image is evident. When training on the original dataset for Approach 1, the
match index (T90) of the distribution of solar storm areas reaches T90 ~ 0.993,
and T90 ~ 0.951 for Approach 2. In addition, when using the extended training
base, the match indices increased to T90 ~ 0.994 and T90 ~ 1, respectively.
This model consistently classifies areas with swirling magnetic lines
associated with solar storms and is robust to image rotation, glare, and
optical artifacts.
