High Power Laser and Particle Beams, Volume. 35, Issue 10, 104002(2023)
Geostationary orbital proton energy spectrum inversion based on machine learning
Figures & Tables(9)
Fig. 1. Distribution of magnetic latitude and cutoffrigidity of all stations
Fig. 3. Fluxes calculated by our model and SVR, BP, LSTM models, in comparison with flux data detected by GOES10 detector in solar minimum
Fig. 4. Fluxes calculated by our model and SVR, BP, LSTM models, in comparison with flux data detected by GOES13 detector in solar maximum
Fig. 5. Calculated fluxes comparison among our model, the CREME96 model, and AP8 model in solar minimum
Fig. 6. Calculated fluxes comparison among our model, the CREME96 model, and AP8 model in solar maximum
Table 1. Dataset overview
View tableView in ArticleTable 1. Dataset overview
total data volume number of data used for the training set number of data used for the test set 8 442 700 7 598 430 844 270
Table 2. Various indicators of different parameter schemes
View tableView in ArticleTable 2. Various indicators of different parameter schemes
indicators MSE RMSE MRE/% default parameter scheme 0.605 0.778 19.6 initial parameter scheme 0.635 0.797 25.5 optimal parameter scheme 0.499 0.706 17.6
Table 3. Comparison of indicators for four machine learning models
View tableView in ArticleTable 3. Comparison of indicators for four machine learning models
model MSE RMSE MRE/% training time/s GA-XGBoost 0.499 0.706 12.5 135 SVR 2.632 1.622 264.6 620 BP 0.641 0.801 84.2 3 625 LSTM 0.131 0.361 17.5 6 390
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Jianfei Chen, Hongtao Zhou, Meihua Fang, Kang Wu, Dingyi Song. Geostationary orbital proton energy spectrum inversion based on machine learning[J]. High Power Laser and Particle Beams, 2023, 35(10): 104002
Paper Information
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Received: May. 29, 2023
Accepted: Sep. 16, 2023
Published Online: Nov. 30, 2023
The Author Email: Fang Meihua (fmh_medphys@nuaa.edu.cn)
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