The accurate prediction of ionospheric Total Electron Content (TEC) is important for global navigation satellite systems (GNSS), satellite communications and other space communications applications. In this study, a prediction model of global IGS-TEC maps are established based on testing several different long short-term memory (LSTM) network (LSTM)-based algorithms to explore a direction that can effectively alleviate the increasing error with prediction time. We find that a Multi-step auxiliary algorithm based prediction model performs best. It can effectively predict the global ionospheric IGS-TEC in the next 6 days (the mean absolute deviation (MAD) and root mean square error (RMSE) are 2.485 and 3.511 TECU, respectively) compared to the IRI (the MAD and RMSE are 4.248 and 5.593 TECU). The analyses of four geomagnetic storm events are completely separate from the time range of the training set, so as to further validate the performance of the model. The International Reference Ionosphere model is used as a reference for the performance of our predictive model, and a rotated persistence is estimated by time-shift algorithm of IGS-TEC. The result suggests that the Multi-step auxiliary prediction model has a good generalization performance and can have a relatively good stability and low error during a geomagnetic storm and quiet time. 

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1029/2021SW002854

Prediction of Global Ionospheric TEC Based on Deep Learning

The accurate prediction of storm-time thermospheric mass density is always critically important and also a challenge. In this paper, an available prediction model is established by Long Short-Term Memory (LSTM)-based ensemble learning algorithms. However, the generalization ability of the deep learning model is often suspicious since training data and testing data are from the same data set in the conventional method. Therefore, in order to objectively validate the performance and generalization of the model, we utilize the GOCE data for training and the SWARM-C data for testing to verify its performance mainly during the geomagnetic storm period. The results show that the LSTM-based ensemble learning model (LELM) is robust under different geomagnetic activity levels and has good generalization ability for the different satellite data set. The prediction accuracy of the LELM is proved to be better than a common-used empirical model (NRLMSISE-00). Thus, our approach provides a promising way to give reliable and stable predictions of thermospheric mass density. 

The Prediction of Storm‐Time Thermospheric Mass Density by LSTM‐Based Ensemble Learning

High energy electrons in planetary radiation belts are a major threat to satellites and communications in deep space applications. In order to predict the variations of energetic electron fluxes for different energy channels, we proposed a new ensemble machine leaning model for differential electron flux from 30 keV to 4 MeV in the Earth's radiation belts based on the RBSP-A observation data from March 2013 to December 2017. The deep neural network (DNN), the convolutional neural network (CNN), the combination of CNN and DNN (CNN&DNN), the linear regression (LR), and the light gradient boosting machine (LightGBM) are among the machine learning models chosen. We carefully compared the electron flux predictions for 20 energy levels and all five models can present valid short-time flux forecasts. The DNN model has the poorest result. The LR model is good for short-term forecasting but not so good for long-term forecasting. The LightGBM ensemble model is highly stable, and it has always outperformed other independent models in terms of forecast accuracy. Then the comparison by adding AE and SYM-H indexes is given and the influence of geomagnetic activity conditions can be negligible for this short-time prediction. Furthermore, we applied these five models on Saturn and finally got very similar prediction results. Our results will be significantly useful in instrument designs and system control of future scientific satellites in deep space explorations. 

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1029/2021SW002969

The Short‐Time Prediction of the Energetic Electron Flux in the Planetary Radiation Belt Based on Stacking Ensemble‐Learning Algorithm

Solar Radio-Burst Forecast Based on a Convolutional Neural Network

Due to the accumulation of solar observational data and the development of data-driven algorithms, deep learning methods are widely applied to build a solar flare forecasting model. Most of the works focus on how to design or select proper deep networks for the forecasting task. Nevertheless, the influence of image resolution on the learning based solar flare forecasting model has not been analyzed and discussed. In this Paper, we investigate the influence of the resolution of magnetograms on the accuracy of solar flare forecasting. We study the active regions by the Solar Dynamics Observatory/Helioseismic and Magnetic Imager (SDO/HMI) magnetograms from 2010 to 2019. Then, we downsample them to get a database containing active regions with several resolutions. Afterwards, three deep neural networks (i) AlexNet, (ii) ResNet-18, and (iii) SqueezeNet are implemented to evaluate the performance of solar flare forecasting compared to different resolutions of magnetogram. In experiments, we first did comparative experiments on our own simulated HMI database with different resolutions. Then we conducted experiments on two selected actual overlapping databases, Hinode–HMI and Michelson Doppler Imager–HMI, to reconfirm our conclusions. The experiment results show that all the selected deep learning networks are insensitive to the resolution to a certain extent. We visualized the regions of interest of the network from an interpretable perspective and found that the deep learning network pays more attention to the global features extracted from active regions that are not sensitive to local information in magnetograms.

https://iopscience.iop.org/article/10.3847/1538-4357/ac99dc/pdf

Deep Learning Based Solar Flare Forecasting Model. II. Influence of Image Resolution

https://robots.iopscience.iop.org/article/10.3847/1538-4365/ac249e/pdf

Xunwen Zeng

Papers

Solar Flare Prediction Based on the Fusion of Multiple Deep-learning Models

Multiple CNN Variants and Ensemble Learning for Sunspot Group Classification by Magnetic Type