Domijan, Katarina and Bloomfield, Shaun D. and Pitié, François
(2019)
Solar Flare Forecasting from Magnetic Feature Properties Generated by the Solar Monitor Active Region Tracker.
Solar Physics, 294 (6).
ISSN 0038-0938
Abstract
We study the predictive capabilities of magnetic-feature properties (MF) gener-
ated by the Solar Monitor Active Region Tracker (
SMART
: Higgins
et al.
in
Adv. Space Res.
47
, 2105,
2011
) for solar-flare forecasting from two datasets: the full dataset of
SMART
de-
tections from 1996 to 2010 which has been previously studied by Ahmed
et al.
(
Solar Phys.
283
, 157,
2013
) and a subset of that dataset that only includes detections that are NOAA ac-
tive regions (ARs). The main contributions of this work are: we use marginal relevance as a
filter feature selection method to identify the most useful
SMART
MF properties for separat-
ing flaring from non-flaring detections and logistic regression to derive classification rules to
predict future observations. For comparison, we employ a Random Forest, Support Vector
Machine, and a set of Deep Neural Network models, as well as
lasso
for feature selection.
Using the linear model with three features we obtain significantly better results (True Skill
Score: TSS
=
0.84) than those reported by Ahmed
et al.
(
Solar Phys.
283
, 157,
2013
)for
the full dataset of
SMART
detections. The same model produced competitive results (TSS
=
0.67) for the dataset of
SMART
detections that are NOAA ARs, which can be compared
to a broader section of flare-forecasting literature. We show that more complex models are
not required for this data.
| Item Type: |
Article
|
| Keywords: |
Flares; Forecasting Flares; Relation to Magnetic Field;
Active Regions; Magnetic Fields; |
| Academic Unit: |
Faculty of Science and Engineering > Mathematics and Statistics |
| Item ID: |
10446 |
| Identification Number: |
https://doi.org/10.1007/s11207-018-1392-4 |
| Depositing User: |
Katarina Domijan
|
| Date Deposited: |
23 Jan 2019 15:32 |
| Journal or Publication Title: |
Solar Physics |
| Publisher: |
Springer VS |
| Refereed: |
No |
| URI: |
|
| Use Licence: |
This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available
here |
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