The Short-Time Prediction of Thermospheric Mass Density Based on Ensemble-Transfer Learning

Abstract

Reliable short-time prediction of thermospheric mass density along the satellite orbit is always essential but challenging for the operation of Low-Earth orbit satellites. In this paper, three machine-learning prediction algorithms are investigated, including the Bidirectional Long Short-Term Memory, the Transformer, and the Light Gradient Boosting Machine (LightGBM) ensemble model of the above models. We use satellite data from CHAMP, GOCE, and SWARM-C to evaluate the robustness and accuracy of different density variations. The comparison demonstrates that all models achieve compelling predictions and are much better than NRLMSISE-00. The LightGBM ensemble model (LE-model) consistently outperforms others in accuracy and stability. Furthermore, when the obtained density data from the newly launched satellites are limited, the trained LE-model can provide a valid prediction for the new satellite orbit by transfer learning. This study offers a promising insight into the short-time prediction of thermospheric mass density using ensemble-transfer learning and may be advantageous to future research on space whether.