Prediction of the Dst Index with Bagging Ensemble-learning Algorithm

Abstract

The Dst index is a commonly geomagnetic index used to measure the strength of geomagnetic activity. The accurate prediction of the Dst index is one of the main subjects of space weather studies. In this study, we use the Bagging ensemble-learning algorithm, which combines three algorithms—the artificial neural network, support vector regression, and long short-term memory network—to predict the Dst index 1–6 hr in advance. Taking solar wind parameters (including the interplanetary total magnetic field, magnetic field B z component, total electric field, solar wind speed, plasma temperature, and proton pressure) as inputs, we establish the Dst index models and complete not only the point prediction but also the interval prediction in forecasting the Dst index. The results show that the root mean square error (rmse) of the point prediction is always lower than 8.0936 nT, the correlation coefficient ( R ) is always higher than 0.8572 and the accuracy of interval prediction is always higher than 90%, implying that our model can improve the accuracy of point prediction and significantly promote the accuracy of interval prediction. In addition, an new proposed metric shows that the Bagging algorithm brings better stability to the model. Our model was also used to predicate a magnetic storm event from 2016 October 12–17. The most accurate prediction of this storm event is the 1 hr ahead prediction, which holds a result with the rmse of 3.7327 nT, the correlation coefficient of 0.9928, and the interval prediction accuracy of 96.69%. Moreover, we also discuss the balance in the Bagging ensemble model in this paper.