A novel short-term radio flux trend prediction model based on deep learning
文献类型：期刊论文
| 作者 | Zheng, Yanfang1; Ling, Yi1; Li, Xuebao1; Qin, Weishu1; Dong L(董亮)2,3 ; Huang, Xusheng1; Li, Xuefeng1; Yan, Pengchao1; Yan, Shuainan1; Lou, Hengrui1
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| 刊名 | ASTROPHYSICS AND SPACE SCIENCE
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| 出版日期 | 2023-10 |
| 卷号 | 368期号:10 |
| 关键词 | Sun Solar activity Solar radio flux |
| ISSN号 | 0004-640X |
| DOI | 10.1007/s10509-023-04246-7 |
| 产权排序 | 第2完成单位 |
| 文献子类 | Article |
| 英文摘要 | Solar radio flux is an important indicator of solar activity and solar UV burst. Accurate prediction of solar radio flux plays a crucial role in preventing and mitigating the impact of solar activity on human productivity. We propose a novel approach for the first time to predict short-term radio flux trends using a bidirectional long short-term memory (BLSTM) network. This approach aims to address the unique characteristics of temporality and nonlinearity observed in solar radio flux data. Our model takes into account various frequency characteristics that impact radio flux. This allows it to learn temporal patterns within the data, ultimately enabling accurate predictions of radio flux for the next 30 minutes. The proposed method is experimentally applied to the radio flux dataset of the US Radio Solar Telescope Network (RSTN). The results show that, in most frequency bands, the BLSTM model exhibits superior prediction accuracy and greater sensitivity to peak responses compared to the LSTM model, LSTM-Attention (LSTM-A) model, BLSTM-Attention (BLSTM-A) model, and persistence model (PM). Consequently, the BLSTM model is better equipped to accurately forecast changes in radio flux for the next 30 minutes. |
| 学科主题 | 天文学 |
| URL标识 | 查看原文 |
| 出版地 | VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS |
| WOS关键词 | CORONAL ROTATION ; SOLAR ; MULTIFREQUENCY |
| 资助项目 | We would like to thank the anonymous referees for their valuable suggestions and comments, which significantly improved this work. We are grateful to the Australian Space Weather Forecasting Centre for the provision of Solar Radio data. |
| WOS研究方向 | Astronomy & Astrophysics |
| 语种 | 英语 |
| WOS记录号 | WOS:001090545300001 |
| 出版者 | SPRINGER |
| 资助机构 | We would like to thank the anonymous referees for their valuable suggestions and comments, which significantly improved this work. We are grateful to the Australian Space Weather Forecasting Centre for the provision of Solar Radio data. |
| 版本 | 出版稿 |
| 源URL | [http://ir.ynao.ac.cn/handle/114a53/26422]  |
| 专题 | 云南天文台_射电天文研究组 |
| 作者单位 | 1.School of Automation, Jiangsu University of Science and Technology, Zhenjiang, 212100, Jiangsu, China; 2.Yunnan Sino-Malaysian International Joint Laboratory of HF-VHF Advanced Radio Astronomy Technology, Kunming, 650216, Yunnan, China 3.Yunnan Astronomical Observatory, Chinese Academy of Sciences, Kunming, 650216, Yunnan, China; |
| 推荐引用方式 GB/T 7714 | Zheng, Yanfang,Ling, Yi,Li, Xuebao,et al. A novel short-term radio flux trend prediction model based on deep learning[J]. ASTROPHYSICS AND SPACE SCIENCE,2023,368(10). |
| APA | Zheng, Yanfang.,Ling, Yi.,Li, Xuebao.,Qin, Weishu.,董亮.,...&Ye, Hongwei.(2023).A novel short-term radio flux trend prediction model based on deep learning.ASTROPHYSICS AND SPACE SCIENCE,368(10). |
| MLA | Zheng, Yanfang,et al."A novel short-term radio flux trend prediction model based on deep learning".ASTROPHYSICS AND SPACE SCIENCE 368.10(2023). |
入库方式： OAI收割
来源：云南天文台
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