Solar Wind Speed Prediction With Two‐Dimensional Attention Mechanism
Abstract As more and more high‐technical systems are exposed to the space environment, extreme space weather becomes a great threat to human society. In the solar system, space weather is influenced by the solar wind, such that reliable prediction of solar wind conditions in the near‐Earth environme...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-07-01
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| Series: | Space Weather |
| Online Access: | https://doi.org/10.1029/2020SW002707 |
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| _version_ | 1850118520309809152 |
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| author | Yanru Sun Zongxia Xie Yanhong Chen Xin Huang Qinghua Hu |
| author_facet | Yanru Sun Zongxia Xie Yanhong Chen Xin Huang Qinghua Hu |
| author_sort | Yanru Sun |
| collection | DOAJ |
| description | Abstract As more and more high‐technical systems are exposed to the space environment, extreme space weather becomes a great threat to human society. In the solar system, space weather is influenced by the solar wind, such that reliable prediction of solar wind conditions in the near‐Earth environment effectively reduces the impact of space weather on human society. Solar wind speed prediction is improved by making full use of OMNI data measured at Lagrangian Point 1 (L1) by the National Aeronautics and Space Administration (NASA) and image data observed by the Solar Dynamics Observatory (SDO) satellite in this work. Specifically, we propose a model based on the “two‐dimensional attention mechanism” (TDAM) to predict solar wind speed. In this study, we first analyze and preprocess data from 2011 to 2017. Second, considering the characteristics of time series data, we adopt the gated recurrent units (GRU) model which can deal with long‐term dependence as the prediction part of our model. Third, we design a TDAM, which enables our prediction network to focus on important parts. Three performance indices are used: root‐mean‐square error (RMSE), mean absolute error (MAE), and correlation coefficient (CC). By comparing TDAM with other models, we find that the TDAM model achieves the best prediction results, with RMSE of 62.8 km/s, MAE of 47.8 km/s, and CC of 0.789 24 h in advance. The experimental results show that the proposed TDAM model can improve the prediction accuracy of solar wind speed. |
| format | Article |
| id | doaj-art-df6926f98d494e45a37323deedf85527 |
| institution | OA Journals |
| issn | 1542-7390 |
| language | English |
| publishDate | 2021-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-df6926f98d494e45a37323deedf855272025-08-20T02:35:51ZengWileySpace Weather1542-73902021-07-01197n/an/a10.1029/2020SW002707Solar Wind Speed Prediction With Two‐Dimensional Attention MechanismYanru Sun0Zongxia Xie1Yanhong Chen2Xin Huang3Qinghua Hu4College of Intelligence and Computing Tianjin University Tianjin ChinaCollege of Intelligence and Computing Tianjin University Tianjin ChinaNational Space Science Center Chinese Academy of Sciences Beijing ChinaNational Astronomical Observatories Chinese Academy of Sciences Beijing ChinaCollege of Intelligence and Computing Tianjin University Tianjin ChinaAbstract As more and more high‐technical systems are exposed to the space environment, extreme space weather becomes a great threat to human society. In the solar system, space weather is influenced by the solar wind, such that reliable prediction of solar wind conditions in the near‐Earth environment effectively reduces the impact of space weather on human society. Solar wind speed prediction is improved by making full use of OMNI data measured at Lagrangian Point 1 (L1) by the National Aeronautics and Space Administration (NASA) and image data observed by the Solar Dynamics Observatory (SDO) satellite in this work. Specifically, we propose a model based on the “two‐dimensional attention mechanism” (TDAM) to predict solar wind speed. In this study, we first analyze and preprocess data from 2011 to 2017. Second, considering the characteristics of time series data, we adopt the gated recurrent units (GRU) model which can deal with long‐term dependence as the prediction part of our model. Third, we design a TDAM, which enables our prediction network to focus on important parts. Three performance indices are used: root‐mean‐square error (RMSE), mean absolute error (MAE), and correlation coefficient (CC). By comparing TDAM with other models, we find that the TDAM model achieves the best prediction results, with RMSE of 62.8 km/s, MAE of 47.8 km/s, and CC of 0.789 24 h in advance. The experimental results show that the proposed TDAM model can improve the prediction accuracy of solar wind speed.https://doi.org/10.1029/2020SW002707 |
| spellingShingle | Yanru Sun Zongxia Xie Yanhong Chen Xin Huang Qinghua Hu Solar Wind Speed Prediction With Two‐Dimensional Attention Mechanism Space Weather |
| title | Solar Wind Speed Prediction With Two‐Dimensional Attention Mechanism |
| title_full | Solar Wind Speed Prediction With Two‐Dimensional Attention Mechanism |
| title_fullStr | Solar Wind Speed Prediction With Two‐Dimensional Attention Mechanism |
| title_full_unstemmed | Solar Wind Speed Prediction With Two‐Dimensional Attention Mechanism |
| title_short | Solar Wind Speed Prediction With Two‐Dimensional Attention Mechanism |
| title_sort | solar wind speed prediction with two dimensional attention mechanism |
| url | https://doi.org/10.1029/2020SW002707 |
| work_keys_str_mv | AT yanrusun solarwindspeedpredictionwithtwodimensionalattentionmechanism AT zongxiaxie solarwindspeedpredictionwithtwodimensionalattentionmechanism AT yanhongchen solarwindspeedpredictionwithtwodimensionalattentionmechanism AT xinhuang solarwindspeedpredictionwithtwodimensionalattentionmechanism AT qinghuahu solarwindspeedpredictionwithtwodimensionalattentionmechanism |