Ionospheric TEC Forecasting with ED-ConvLSTM-Res Integrating Multi-Channel Features

This paper proposes a convolutional Long Short-Term Memory (ConvLSTM) network integrated with multi-channel features dedicated to ionospheric total electron content (TEC) forecasting. To improve generalization, solar, and geomagnetic activity indices are added as auxiliary channel inputs. The model is built upon an Encoder–Decoder (ED) architecture enhanced with residual connections and convolutional channel projection, which collectively improve the synergy among its core components. Based on this framework, we developed ED-ConvLSTM-Res, a multi-channel feature-based global ionospheric TEC prediction model. Comprehensive accuracy evaluation and comparative tests were carried out using datasets from the solar minimum year of 2019 and the current solar maximum year of 2024. The results indicate that the proposed model consistently achieves strong predictive performance compared with other models, along with a significantly enhanced feature representation capability. Specifically, the Root Mean Square Errors (RMSE) of the ED-ConvLSTM-Res model’s predictions in 2019 and 2024 are 1.28 TECU and 5.28 TECU, respectively, while the corresponding Mean Absolute Errors (MAE) are 0.87 and 3.87, and the coefficients of determination (R2) are 0.95 and 0.94. In the current high solar activity year 2024, the proposed model achieves error reductions of 13.6% in MAE and 11.6% in RMSE compared with the Center for Orbit Determination in Europe (CODE)’s one-day-ahead forecast product, c1pg. These results confirm that the proposed model not only outperforms the ConvLSTM model without additional indices and c1pg but also exhibits strong generalization capability, maintaining stable performance with low errors under both high and low solar activity conditions.