The Prediction of Storm-Time Thermospheric Mass Density by LSTM-Based Ensemble Learning

Abstract

The accurate prediction of storm-time thermospheric mass density is always critically important and also a challenge. In this paper, an available prediction model is established by Long Short-Term Memory (LSTM)-based ensemble learning algorithms. However, the generalization ability of the deep learning model is often suspicious since training data and testing data are from the same data set in the conventional method. Therefore, in order to objectively validate the performance and generalization of the model, we utilize the GOCE data for training and the SWARM-C data for testing to verify its performance mainly during the geomagnetic storm period. The results show that the LSTM-based ensemble learning model (LELM) is robust under different geomagnetic activity levels and has good generalization ability for the different satellite data set. The prediction accuracy of the LELM is proved to be better than a common-used empirical model (NRLMSISE-00). Thus, our approach provides a promising way to give reliable and stable predictions of thermospheric mass density.