Machine learning in solar physics
A Asensio Ramos, MCM Cheung, I Chifu… - Living Reviews in Solar …, 2023 - Springer
The application of machine learning in solar physics has the potential to greatly enhance our
understanding of the complex processes that take place in the atmosphere of the Sun. By …
understanding of the complex processes that take place in the atmosphere of the Sun. By …
A comparative study of non-deep learning, deep learning, and ensemble learning methods for sunspot number prediction
Solar activity has significant impacts on human activities and health. One most commonly
used measure of solar activity is the sunspot number. This paper compares three important …
used measure of solar activity is the sunspot number. This paper compares three important …
Predicting solar cycle 25 using an optimized long short-term memory model based on sunspot area data
H Zhu, H Chen, W Zhu, M He - Advances in Space Research, 2023 - Elsevier
In this paper, an optimized long short-term memory (LSTM) model was proposed to deal with
the monthly sunspot area (SSA) data, aiming to predict the peak amplitude of SSA and the …
the monthly sunspot area (SSA) data, aiming to predict the peak amplitude of SSA and the …
Machine learning in solar physics
The application of machine learning in solar physics has the potential to greatly enhance our
understanding of the complex processes that take place in the atmosphere of the Sun. By …
understanding of the complex processes that take place in the atmosphere of the Sun. By …
Solar cycle 25 prediction using an optimized long short-term memory mode with F10. 7
H Zhu, W Zhu, M He - Solar Physics, 2022 - Springer
In this paper, an optimized long short-term memory (LSTM) model is proposed to deal with
the smoothed monthly F 10.7 data, aiming to predict the peak amplitude of F 10.7 and the …
the smoothed monthly F 10.7 data, aiming to predict the peak amplitude of F 10.7 and the …
A critical comment on “can solar cycle 25 be a new Dalton Minimum?”
JC Peguero, VMS Carrasco - Solar Physics, 2023 - Springer
The sunspot number is the most used solar-activity index to study the behavior of solar
activity. In this work, we reproduce the methodology of Coban, Raheem, and Cavus (Solar …
activity. In this work, we reproduce the methodology of Coban, Raheem, and Cavus (Solar …
Performance Comparison of Bayesian Deep Learning Model and Traditional Bayesian Neural Network in Short-Term PV Interval Prediction
K Wang, H Du, R Jia, H Jia - Sustainability, 2022 - mdpi.com
The intermittence and fluctuation of renewable energy bring significant uncertainty to the
power system, which enormously increases the operational risks of the power system. The …
power system, which enormously increases the operational risks of the power system. The …
Forecasting the solar cycle 25 using a multistep Bayesian neural network
I Bizzarri, D Barghini, S Mancuso… - Monthly Notices of …, 2022 - academic.oup.com
Predicting the solar activity of upcoming cycles is crucial nowadays to anticipate potentially
adverse space weather effects on the Earth's environment produced by coronal transients …
adverse space weather effects on the Earth's environment produced by coronal transients …
Stacked 1D Convolutional LSTM (sConvLSTM1D) Model for Effective Prediction of Sunspot Time Series
A multi-layer, deep-learning (DL) architecture consisting of stacked Convolutional Long
Short Term Memory (sConvLSTM1D) layers is proposed to forecast the sunspot number …
Short Term Memory (sConvLSTM1D) layers is proposed to forecast the sunspot number …
An improved prediction of solar cycle 25 using deep learning based neural network
A deep-learning Vanilla, or single layer, Long Short-Term Memory model is proposed for
improving the prediction of Solar Cycle 25. WDC-SILSO the Royal Observatory of Belgium …
improving the prediction of Solar Cycle 25. WDC-SILSO the Royal Observatory of Belgium …