Highlights A DC-Gaussian process regression model is used to the prediction of Dst index. DC-GPR model has the best stability among the three models among DC-GPR, DC-SVM, and DC-NN models DC-GPR model exhibits the better forecasting performance than DC-SVM and DC-NN models in Dst forecasting.
Abstract In this paper we apply a class of Bayesian machine learning model, Gaussian Process Regression, to the prediction of Dst index by using 80 intense geomagnetic storms data () from 1995 to 2014. The purpose of this paper is to compare the performance of Gaussian process regression model with Support Vector Machine model combined together with Distance Correlation (DC-SVM) and Neural Network model combined together with Distance Correlation (DC-NN) (Lu et al., 2016). For comparison, we estimate the correlation coefficients (CC), the RMS errors, the absolute value of difference in minimum Dst () and the absolute value of difference in minimum time () between observed Dst and predicted one.In order to compare the prediction effects and the generalizability of the three models to magnetic storm events, we combined 70 intense magnetic storm events and 10 super large magnetic storm events into one group. It is shown that DC-GPR model exhibits the better forecasting performance than by DC-SVM model and DC-NN model in magnetic storm. The CC, the RMS errors, the , and the of GPR are 0.65, 35.45 nT, 16.12 nT and 1.16 h, respectively.
A class of Bayesian machine learning model for forecasting Dst during intense geomagnetic storms
Advances in Space Research ; 72 , 9 ; 3882-3889
2023-07-05
8 pages
Article (Journal)
Electronic Resource
English
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