Hao Liu
1,2, Chang Liu
1,3,4,
Jason T. L. Wang
1,2, and Haimin Wang
1,3,4
1. Institute for Space Weather Sciences, New Jersey Institute of
Technology
2. Department of Computer Science, New Jersey Institute of
Technology
3. Big Bear Solar Observatory, New Jersey Institute of Technology
4. Center for Solar-Terrestrial Research, New Jersey Institute of
Technology
Abstract
We present a long short-term memory (LSTM) network for
predicting whether an active region (AR) would produce a
ϒ-class flare within the next 24 hours.
We consider three ϒ classes, namely
≥M5.0 class, ≥M class, and ≥C class, and
build three LSTM models separately,
each corresponding to a ϒ class.
Each LSTM model is used to make predictions of its corresponding
ϒ-class flares.
The essence of our approach is to model data samples in an AR as time
series and
use LSTMs to capture temporal information of the data samples.
Each data sample has 40 features including 25 magnetic parameters
obtained from the Space-weather HMI Active Region Patches (SHARP)
and related data products as well as 15 flare history parameters.
We survey the flare events that occurred from 2010 May to 2018 May,
using the GOES X-ray flare
catalogs provided by the
National Centers for Environmental
Information (NCEI),
and select flares with identified ARs in the NCEI flare catalogs.
These flare events are used to build the labels (positive vs. negative)
of the data samples.
Experimental results show that (i) using only 14-22 most important
features including both flare history and magnetic parameters
can achieve better performance than using all the 40 features together;
(ii) our LSTM network outperforms related machine learning methods
in predicting the labels of the data samples.
To our knowledge, this is the first time that LSTMs have been used for
solar flare prediction.
Datasets and
Source Code
»
Click
here to download
the collection of 4,203 B-class flares, 6,768
C-class flares, 704 M-class flares,
and 49 X-class flares used to build the
data samples studied in the paper.
»
Click
M5,
M,
C to download the data samples
in the
≥M5.0 class, ≥M class, and ≥C class respectively
described in
Table 2 in the paper.
Click
here
to see the explanation of the downloaded files.
Click
here to download
the raw data used to build the data samples described in
Table 2.
» Click
here
to download our Python program, run on GPUs,
and a sample dataset used to generate
the performance metric values
for the ≥C class in Table 5 in the paper.
» Click
here
to download our DEMONSTRATION Python programs, run on GPUs,
and sample datasets where
you can run the programs on the datasets
and see how our programs make predictions.
Reference
Predicting Solar Flares
Using a Long Short-term Memory Network.
Liu, H., Liu, C., Wang, J. T. L., Wang, H., ApJ., 877:121, 2019
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