Predicting the Daily 10.7-cm Solar Radio Flux Using the Long Short-Term Memory Method

Zhang, Wanting; Zhao, Xinhua; Feng, Xueshang; Liu, Cheng’ao; Xiang, N. B.; Zheng, Li; Lu, Wei

doi:10.3390/universe8010030

Cited by 18 publications

(9 citation statements)

References 32 publications

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“…To better evaluate the prediction performance of the VMD‐LSTM model, we compare its results with those of the LSTM model (W. Zhang et al., 2022), BP models (Xiao et al., 2017), and AR models (Du, 2020). The BP model uses the output error to estimate the error of the direct leading layer in the output layer and then uses this error to estimate the error of the previous layer.…”

Section: Resultsmentioning

confidence: 99%

“…Gao et al (2022) combined the sunspots number into LSTM to make a short-term prediction method for F 10.7 in the next 7 days based on a 54-day solar radiation flux index, with a root mean square error (RMSE) 11% lower than that of the Space Weather Prediction Center (SWPC) in America. W. Zhang et al (2022) used the LSTM model for short-term prediction of the F 10.7 index and verified that the LSTM model outperformed ordinary neural network models.…”

mentioning

confidence: 93%

“…W. Zhang et al. (2022) used the LSTM model for short‐term prediction of the F 10.7 index and verified that the LSTM model outperformed ordinary neural network models.…”

Section: Introductionmentioning

confidence: 98%

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F_10.7 Daily Forecast Using LSTM Combined With VMD Method

Hao,

Lu,

Peng

et al. 2024

Space Weather

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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.

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Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 93%

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F_10.7 Daily Forecast Using LSTM Combined With VMD Method

Hao,

Lu,

Peng

et al. 2024

Space Weather

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“…-Solar activity: solar activity significantly influences TEC values, with lower TEC during solar minimum years and higher TEC during solar maximum years [21]. F10.7, which correlates directly with the number of sunspots and solar radiation in the ultraviolet and visible spectrum, serves as a valuable predictor of solar activity [22]. -Geomagnetic activity (Dst, Kp, and Ap index): the Dst, Kp, and Ap represent geomagnetic activity.…”

Section: Methods 21 Data and Input Parametersmentioning

confidence: 99%

Prediction of ionospheric total electron content data using NARX neural network model

Shenvi,

Virani

2024

Bulletin EEI

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Successful prediction of ionospheric total electron content (TEC) data will help in correction of positioning errors in global navigation satellite systems (GNSS) caused by the ionosphere. This research paper proposes a prediction model for ionospheric TEC using a nonlinear autoregressive with exogenous inputs (NARX) neural network that utilizes past TEC data alongwith solar and geomagnetic indices namely F10.7, disturbed storm (Dst), Kp, Ap, and time of the day. We assess the prediction capability of our model at different latitudes during different solar activity years. We compare our results with another NARX model which uses previous TEC data along with time of the day, day of the year and season as exogenous parameters. The results show that for the solar minimum year the TEC prediction accuracy improves by 35.71% and for the solar maximum year it improves by 31.20%. The results using root mean square error (RMSE), mean absolute error (MAE), correlation coefficient, and symmetric mean absolute percentage error (sMAPE) clearly indicate that solar and geomagnetic indices along with time of the day help in enhancing prediction accuracy of TEC across different latitudinal regions during both solar minimum and maximum years.

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“…Everyday measurements have been publicly available since 1947 (Tapping, 2013). Also, the reliable forecast of the F10.7 index (Gaidash et al 2017;Huang et al, 2009;Lei et al, 2019;Henney et al, 2012;Zhang et al, 2022) gives an opportunity to predict the upper atmosphere state up to 55 days.…”

Section: F107 Indexmentioning

confidence: 99%

SPAM: Solar Spectrum Prediction for Applications and Modeling

Nikolaeva

Gordeev

2022

Preprint

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Solar Spectrum Prediction for Applications and Modeling (SPAM) -a new empirical model of solar X-Ray, EUV and FUV radiation flux at the top of the Earth's atmosphere. The model is based on 14 years of daily averaged TIMED spacecraft measurements from 2002 to 2016 when the SEE sensors were regularly calibrated. We used a second-order parametrization of the irradiance spectrum by a single parameter -the F10.7 index, which is a reliable and consistently observed measure of solar activity. The SPAM model consists of two submodels for general and specific use. The first is the Solar-SPAM model of the photon energy flux in the first 190 1nm spectral bands, which can be used for a wide range of applications in different fields of research. The second model, Aero-SPAM, is designed specifically for aeronomic research and provides a photon flux for 37 specific wavelength intervals (20 wave bands and 16 separate spectral lines within the range of 5 -105 nm and an additional 121.5 nm Ly-alpha line), that play a major role in the photoionization of atmospheric gas particles. We provide the full set of parameterization coefficients that allows for immediate implementation of the model for research and applications. In addition, we used the Aero-SPAM model to build a ready-to-use numerical application for calculating the photoionization rates of the main atmospheric components N2, O2, O, N and NO with known absorption and ionization cross sections.

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Predicting the Daily 10.7-cm Solar Radio Flux Using the Long Short-Term Memory Method

Cited by 18 publications

References 32 publications

F_10.7 Daily Forecast Using LSTM Combined With VMD Method

F_10.7 Daily Forecast Using LSTM Combined With VMD Method

Prediction of ionospheric total electron content data using NARX neural network model

SPAM: Solar Spectrum Prediction for Applications and Modeling

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Predicting the Daily 10.7-cm Solar Radio Flux Using the Long Short-Term Memory Method

Cited by 18 publications

References 32 publications

F10.7 Daily Forecast Using LSTM Combined With VMD Method

F10.7 Daily Forecast Using LSTM Combined With VMD Method

Prediction of ionospheric total electron content data using NARX neural network model

SPAM: Solar Spectrum Prediction for Applications and Modeling

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Product

Resources

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F_10.7 Daily Forecast Using LSTM Combined With VMD Method

F_10.7 Daily Forecast Using LSTM Combined With VMD Method