Prediction of Partial Ring Current Index Using LSTM Neural Network

The local time dependence of the geomagnetic disturbances during magnetic storms indicates the necessity of forecasting the localized magnetic storm indices. For the first time, we construct prediction models for the SuperMAG partial ring current indices (SMR-LT), with the advance time increasing from 1 h to 12 h by Long Short-Term Memory (LSTM) neural network. Generally, the prediction performance decreases with the advance time and is better for the SMR-06 index than for the SMR-00, SMR-12, and SMR-18 index. For the predictions with 12 h ahead, the correlation coefficient is 0.738, 0.608, 0.665, and 0.613, respectively. To avoid the over-represented effect of massive data during geomagnetic quiet periods, only the data during magnetic storms are used to train and test our models, and the improvement in prediction metrics increases with the advance time. For example, for predicting the storm-time SMR-06 index with 12 h ahead, the correlation coefficient and the prediction efficiency increases from 0.674 to 0.691, and from 0.349 to 0.455, respectively. The evaluation of the model performance for forecasting the storm intensity shows that the relative error for intense storms is usually less than the relative error for moderate storms.