{"title":"F10.7 使用 LSTM 结合 VMD 方法进行每日预测","authors":"Yuhang Hao, Jianyong Lu, Guangshuai Peng, Ming Wang, Jingyuan Li, Guanchun Wei","doi":"10.1029/2023sw003552","DOIUrl":null,"url":null,"abstract":"The <i>F</i><sub>10.7</sub> solar radiation flux is a well-known parameter that is closely linked to solar activity, serving as a key index for measuring the level of solar activity. In this study, the Variational Mode Decomposition (VMD) and Long Short-term Memory (LSTM) network are combined to construct a VMD-LSTM model for predicting <i>F</i><sub>10.7</sub> values. The <i>F</i><sub>10.7</sub> sequence is decomposed into several intrinsic mode functions (IMF) by VMD, then the LSTM neural network is utilized to forecast each IMF. All IMF prediction results are aggregated to obtain the final <i>F</i><sub>10.7</sub> value. The data sets from 1957 to 2008 are used for training and the data sets from 2009 to 2019 are used for testing. The results show that the VMD-LSTM model achieves an annual average root mean square error of only 4.47 sfu and an annual average correlation coefficient (<i>R</i>) of 0.99 during solar cycle 24, which is significantly better than the accuracy of the LSTM model (W. Zhang et al., 2022, https://doi.org/10.3390/universe8010030), the AR model (Du, 2020, https://doi.org/10.1007/s11207-020-01689-x), and the BP model (Xiao et al., 2017, https://doi.org/10.11728/cjss2017.01.001). The VMD-LSTM model exhibits strong predictive capability for the <i>F</i><sub>10.7</sub> index during solar cycle 24.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":null,"pages":null},"PeriodicalIF":3.7000,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"F10.7 Daily Forecast Using LSTM Combined With VMD Method\",\"authors\":\"Yuhang Hao, Jianyong Lu, Guangshuai Peng, Ming Wang, Jingyuan Li, Guanchun Wei\",\"doi\":\"10.1029/2023sw003552\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The <i>F</i><sub>10.7</sub> solar radiation flux is a well-known parameter that is closely linked to solar activity, serving as a key index for measuring the level of solar activity. In this study, the Variational Mode Decomposition (VMD) and Long Short-term Memory (LSTM) network are combined to construct a VMD-LSTM model for predicting <i>F</i><sub>10.7</sub> values. The <i>F</i><sub>10.7</sub> sequence is decomposed into several intrinsic mode functions (IMF) by VMD, then the LSTM neural network is utilized to forecast each IMF. All IMF prediction results are aggregated to obtain the final <i>F</i><sub>10.7</sub> value. The data sets from 1957 to 2008 are used for training and the data sets from 2009 to 2019 are used for testing. The results show that the VMD-LSTM model achieves an annual average root mean square error of only 4.47 sfu and an annual average correlation coefficient (<i>R</i>) of 0.99 during solar cycle 24, which is significantly better than the accuracy of the LSTM model (W. Zhang et al., 2022, https://doi.org/10.3390/universe8010030), the AR model (Du, 2020, https://doi.org/10.1007/s11207-020-01689-x), and the BP model (Xiao et al., 2017, https://doi.org/10.11728/cjss2017.01.001). The VMD-LSTM model exhibits strong predictive capability for the <i>F</i><sub>10.7</sub> index during solar cycle 24.\",\"PeriodicalId\":22181,\"journal\":{\"name\":\"Space Weather\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2024-01-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Space Weather\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.1029/2023sw003552\",\"RegionNum\":2,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Space Weather","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1029/2023sw003552","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
F10.7 Daily Forecast Using LSTM Combined With VMD Method
The F10.7 solar radiation flux is a well-known parameter that is closely linked to solar activity, serving as a key index for measuring the level of solar activity. In this study, the Variational Mode Decomposition (VMD) and Long Short-term Memory (LSTM) network are combined to construct a VMD-LSTM model for predicting F10.7 values. The F10.7 sequence is decomposed into several intrinsic mode functions (IMF) by VMD, then the LSTM neural network is utilized to forecast each IMF. All IMF prediction results are aggregated to obtain the final F10.7 value. The data sets from 1957 to 2008 are used for training and the data sets from 2009 to 2019 are used for testing. The results show that the VMD-LSTM model achieves an annual average root mean square error of only 4.47 sfu and an annual average correlation coefficient (R) of 0.99 during solar cycle 24, which is significantly better than the accuracy of the LSTM model (W. Zhang et al., 2022, https://doi.org/10.3390/universe8010030), the AR model (Du, 2020, https://doi.org/10.1007/s11207-020-01689-x), and the BP model (Xiao et al., 2017, https://doi.org/10.11728/cjss2017.01.001). The VMD-LSTM model exhibits strong predictive capability for the F10.7 index during solar cycle 24.