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Abstract Many factors influence relativistic outer radiation belt electron fluxes, such as waves in the ultralow frequency (ULF) Pc5, very low frequency (VLF), and electromagnetic ion cyclotron (EMIC) frequency bands, seed electron flux, Dst disturbance levels, substorm occurrence, and solar wind inputs. In this work we compared relativistic electron flux poststorm versus prestorm using three methods of analysis: (1) multiple regression to predict flux values following storms, (2) multiple regression to predict the size and direction of the change in electron flux, and (3) multiple logistic regression to predict only the probability of the flux rising or falling. We determined which is the most predictive model and which factors are most influential. We found that a linear regression predicting the difference in prestorm and poststorm flux (Model 2) results in the highest validation correlations. The logistic regression used in Model 3 had slightly weaker predictive abilities than the other two models but had the most value in providing a prediction of the probability of the electron flux increasing after a storm. Of the variables used (ULF Pc5 and VLF, seed electrons, substorm activity, and EMIC waves), the most influential in the final model were ULF Pc5 waves and the seed electrons. IMF Bz, Dst, and solar wind number density, velocity, and pressure did not improve any of the models, and were deemed unnecessary for effective predictions.
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Classifier Neural Network Models Predict Relativistic Electron Events at Geosynchronous Orbit Better than Multiple Regression or ARMAX Models
Abstract To find the best method of predicting when daily relativistic electron flux (>2 MeV) will rise at geosynchronous orbit, we compare model predictive success rates (true positive rate or TPR) for multiple regression, ARMAX, logistic regression, a feed‐forward multilayer perceptron (MLP), and a recurrent neural network (RNN) model. We use only those days on which flux could rise, removing days when flux is already high from the data set. We explore three input variable sets: (1) ground‐based data (Kp,Dst, and sunspot number), (2) a full set of easily available solar wind and interplanetary magnetic field parameters (|B|,Bz,V,N,P,Ey,Kp,Dst, and sunspot number, and (3) this full set with the addition of previous day's flux. Despite high validation correlations in the multiple regression and ARMAX predictions, these regression models had low predictive ability (TPR < 45%) and are not recommended for use. The three classifier model types (logistic regression, MLP, and RNN) performed better (TPR: 50.8–74.6%). These rates were increased further if the cost of missing an event was set at 4 times that of predicting an event that did not happen (TPR: 73.1–89.6%). The area under the receiver operating characteristic curves did not, for the most part, differ between the classifier models (logistic, MLP, and RNN), indicating that any of the three could be used to discriminate between events and nonevents, but validation suggests a full RNN model performs best. The addition of previous day's flux as a predictor provided only a slight advantage.
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- Award ID(s):
- 1651263
- PAR ID:
- 10454535
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Space Physics
- Volume:
- 125
- Issue:
- 5
- ISSN:
- 2169-9380
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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