Forecasting Major Flares Using Magnetograms and Knowledge-informed Features: A Comparative Study of Deep Learning Models with Generalization to Multiple Data Products
In this study, we construct two kinds of data sets from distinct time periods, both comprising line-of-sight magnetograms and knowledge-informed features. We develop eight models for forecasting ≥M-class flares within 24 hr, including the image-based convolutional neural network (CNN), CNN-BiLSTM, C...
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IOP Publishing
2025-01-01
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| Series: | The Astrophysical Journal Supplement Series |
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| Online Access: | https://doi.org/10.3847/1538-4365/ade687 |
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| author | Xuebao Li Shunhuang Zhang Yanfang Zheng Ting Li Rui Wang Yingbo Liu Hongwei Ye Noraisyah Mohamed Shah Pengchao Yan Xuefeng Li Xiaotian Wang Yongshang Lv Jinfang Wei Honglei Jin Changtian Xiang |
| author_facet | Xuebao Li Shunhuang Zhang Yanfang Zheng Ting Li Rui Wang Yingbo Liu Hongwei Ye Noraisyah Mohamed Shah Pengchao Yan Xuefeng Li Xiaotian Wang Yongshang Lv Jinfang Wei Honglei Jin Changtian Xiang |
| author_sort | Xuebao Li |
| collection | DOAJ |
| description | In this study, we construct two kinds of data sets from distinct time periods, both comprising line-of-sight magnetograms and knowledge-informed features. We develop eight models for forecasting ≥M-class flares within 24 hr, including the image-based convolutional neural network (CNN), CNN-BiLSTM, CNN-BiLSTM-Attention, and Vision Transformer models, as well as the knowledge-informed neural network, BiLSTM, BiLSTM-Attention, and iTransformer models. We analyze the importance of knowledge-informed features by assessing categorical and probabilistic performance using the true skill statistic (TSS) and the Brier skill score (BSS), respectively. This is the first time the iTransformer has been applied to flare forecasting. Subsequently, we compare the forecasting performance of the eight models. Then, we investigate the generalization ability of the models across three different data products. Finally, we fairly compare the forecasting performance of iTransformer with that of the currently advanced NASA/CCMC models. The major results are as follows. (1) The R_VALUE feature consistently shows the best performance in both categorical and probabilistic forecasting for the knowledge-informed models. (2) The iTransformer yields the highest forecasting performance, with TSS and BSS scores of 0.768 ± 0.072 and 0.513 ± 0.063, respectively. The knowledge-informed deep learning models consistently outperform image-based models. (3) The three image-based models demonstrate good generalization performance in categorical forecasting on Space-weather Helioseismic and Magnetic Imager (HMI) Active Region Patches (SHARP), HMI, and Full-Disk Magnetograph (FMG), while the two knowledge-informed models exhibit excellent generalization performance on SHARP and HMI. This is the first time that FMG magnetograms and knowledge-informed features are used for flare forecasting. Additionally, the five models also demonstrate strong generalization ability on SHARP across different time periods. (4) The iTransformer exhibits superior forecasting performance compared to NASA/CCMC. |
| format | Article |
| id | doaj-art-a51e4bcd7bef46f2a77a971776a4333e |
| institution | Kabale University |
| issn | 0067-0049 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astrophysical Journal Supplement Series |
| spelling | doaj-art-a51e4bcd7bef46f2a77a971776a4333e2025-08-20T03:56:09ZengIOP PublishingThe Astrophysical Journal Supplement Series0067-00492025-01-0127924610.3847/1538-4365/ade687Forecasting Major Flares Using Magnetograms and Knowledge-informed Features: A Comparative Study of Deep Learning Models with Generalization to Multiple Data ProductsXuebao Li0https://orcid.org/0000-0003-0397-4372Shunhuang Zhang1https://orcid.org/0009-0003-0325-1366Yanfang Zheng2https://orcid.org/0000-0003-0229-3989Ting Li3https://orcid.org/0000-0001-6655-1743Rui Wang4https://orcid.org/0000-0001-5205-1713Yingbo Liu5https://orcid.org/0000-0003-4031-4285Hongwei Ye6https://orcid.org/0009-0009-9722-5794Noraisyah Mohamed Shah7https://orcid.org/0000-0002-1320-8711Pengchao Yan8https://orcid.org/0000-0002-3667-3587Xuefeng Li9https://orcid.org/0009-0000-6353-5473Xiaotian Wang10https://orcid.org/0009-0004-1408-8055Yongshang Lv11https://orcid.org/0009-0004-9110-0425Jinfang Wei12https://orcid.org/0009-0001-3404-3186Honglei Jin13https://orcid.org/0009-0006-5294-3571Changtian Xiang14https://orcid.org/0009-0009-8072-3519School of Computer Science, Jiangsu University of Science and Technology , Zhenjiang 212100, People’s Republic of China ; zyf062856@163.com; State Key Laboratory of Space Weather, Chinese Academy of Sciences , Beijing 100190, People’s Republic of ChinaSchool of Computer Science, Jiangsu University of Science and Technology , Zhenjiang 212100, People’s Republic of China ; zyf062856@163.com; State Key Laboratory of Space Weather, Chinese Academy of Sciences , Beijing 100190, People’s Republic of ChinaSchool of Computer Science, Jiangsu University of Science and Technology , Zhenjiang 212100, People’s Republic of China ; zyf062856@163.com; State Key Laboratory of Space Weather, Chinese Academy of Sciences , Beijing 100190, People’s Republic of ChinaCAS Key Laboratory of Solar Activity, National Astronomical Observations, Chinese Academy of Sciences , Beijing 100101, People’s Republic of China; School of Astronomy and Space Science, University of Chinese Academy of Sciences , Beijing 100049, People’s Republic of China; National Space Science Center, Chinese Academy of Sciences , Beijing 100190, People’s Republic of ChinaState Key Laboratory of Space Weather, Chinese Academy of Sciences , Beijing 100190, People’s Republic of ChinaBig Data Research Institute of Yunnan Economy and Society, Yunnan University of Finance and Economics , Kunming 650221, People’s Republic of China ; liuyb@ynufe.edu.cnSchool of Computer Science, Jiangsu University of Science and Technology , Zhenjiang 212100, People’s Republic of China ; zyf062856@163.com; State Key Laboratory of Space Weather, Chinese Academy of Sciences , Beijing 100190, People’s Republic of ChinaDepartment of Electrical Engineering, Faculty of Engineering, University of Malaya , MalaysiaSchool of Computer Science, Jiangsu University of Science and Technology , Zhenjiang 212100, People’s Republic of China ; zyf062856@163.comSchool of Computer Science, Jiangsu University of Science and Technology , Zhenjiang 212100, People’s Republic of China ; zyf062856@163.comSchool of Computer Science, Jiangsu University of Science and Technology , Zhenjiang 212100, People’s Republic of China ; zyf062856@163.comSchool of Computer Science, Jiangsu University of Science and Technology , Zhenjiang 212100, People’s Republic of China ; zyf062856@163.comSchool of Computer Science, Jiangsu University of Science and Technology , Zhenjiang 212100, People’s Republic of China ; zyf062856@163.comSchool of Computer Science, Jiangsu University of Science and Technology , Zhenjiang 212100, People’s Republic of China ; zyf062856@163.comSchool of Computer Science, Jiangsu University of Science and Technology , Zhenjiang 212100, People’s Republic of China ; zyf062856@163.comIn this study, we construct two kinds of data sets from distinct time periods, both comprising line-of-sight magnetograms and knowledge-informed features. We develop eight models for forecasting ≥M-class flares within 24 hr, including the image-based convolutional neural network (CNN), CNN-BiLSTM, CNN-BiLSTM-Attention, and Vision Transformer models, as well as the knowledge-informed neural network, BiLSTM, BiLSTM-Attention, and iTransformer models. We analyze the importance of knowledge-informed features by assessing categorical and probabilistic performance using the true skill statistic (TSS) and the Brier skill score (BSS), respectively. This is the first time the iTransformer has been applied to flare forecasting. Subsequently, we compare the forecasting performance of the eight models. Then, we investigate the generalization ability of the models across three different data products. Finally, we fairly compare the forecasting performance of iTransformer with that of the currently advanced NASA/CCMC models. The major results are as follows. (1) The R_VALUE feature consistently shows the best performance in both categorical and probabilistic forecasting for the knowledge-informed models. (2) The iTransformer yields the highest forecasting performance, with TSS and BSS scores of 0.768 ± 0.072 and 0.513 ± 0.063, respectively. The knowledge-informed deep learning models consistently outperform image-based models. (3) The three image-based models demonstrate good generalization performance in categorical forecasting on Space-weather Helioseismic and Magnetic Imager (HMI) Active Region Patches (SHARP), HMI, and Full-Disk Magnetograph (FMG), while the two knowledge-informed models exhibit excellent generalization performance on SHARP and HMI. This is the first time that FMG magnetograms and knowledge-informed features are used for flare forecasting. Additionally, the five models also demonstrate strong generalization ability on SHARP across different time periods. (4) The iTransformer exhibits superior forecasting performance compared to NASA/CCMC.https://doi.org/10.3847/1538-4365/ade687Solar active regionsSolar active region magnetic fieldsNeural networksAstronomy image processingSolar flares |
| spellingShingle | Xuebao Li Shunhuang Zhang Yanfang Zheng Ting Li Rui Wang Yingbo Liu Hongwei Ye Noraisyah Mohamed Shah Pengchao Yan Xuefeng Li Xiaotian Wang Yongshang Lv Jinfang Wei Honglei Jin Changtian Xiang Forecasting Major Flares Using Magnetograms and Knowledge-informed Features: A Comparative Study of Deep Learning Models with Generalization to Multiple Data Products The Astrophysical Journal Supplement Series Solar active regions Solar active region magnetic fields Neural networks Astronomy image processing Solar flares |
| title | Forecasting Major Flares Using Magnetograms and Knowledge-informed Features: A Comparative Study of Deep Learning Models with Generalization to Multiple Data Products |
| title_full | Forecasting Major Flares Using Magnetograms and Knowledge-informed Features: A Comparative Study of Deep Learning Models with Generalization to Multiple Data Products |
| title_fullStr | Forecasting Major Flares Using Magnetograms and Knowledge-informed Features: A Comparative Study of Deep Learning Models with Generalization to Multiple Data Products |
| title_full_unstemmed | Forecasting Major Flares Using Magnetograms and Knowledge-informed Features: A Comparative Study of Deep Learning Models with Generalization to Multiple Data Products |
| title_short | Forecasting Major Flares Using Magnetograms and Knowledge-informed Features: A Comparative Study of Deep Learning Models with Generalization to Multiple Data Products |
| title_sort | forecasting major flares using magnetograms and knowledge informed features a comparative study of deep learning models with generalization to multiple data products |
| topic | Solar active regions Solar active region magnetic fields Neural networks Astronomy image processing Solar flares |
| url | https://doi.org/10.3847/1538-4365/ade687 |
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