Validation-Constrained Adaptive Residual Fusion Framework for Low-Latitude TEC Prediction in GNSS Ionospheric Sensing

Accurate prediction of low-latitude total electron content, or TEC, is important for GNSS ionospheric sensing because pronounced diurnal variation and sensitivity to solar and geomagnetic activity make TEC highly nonlinear and nonstationary. This study proposes the RC-XGB-CNN-BiLSTM validation-constrained adaptive residual-fusion framework for one-hour-ahead TEC prediction. XGBoost provides the primary estimate from historical TEC, local-time factors, and solar-terrestrial inputs, while CNN-BiLSTM learns residuals generated from chronologically ordered out-of-fold predictions. Validation performance determines the residual-use mode and compensation strength. Experiments cover multivariate inputs, contrasting solar and geomagnetic activity conditions, module-wise ablation, and additional low-latitude grid points. In the 2019 multivariate experiment at 20° N, 110° E, the framework achieves an RMSE of 0.67 TECU, 20.2% lower than XGBoost, with concurrent reductions in MAE and P90AE. It also achieves the lowest errors among the compared models in both solar-activity experiments and at all three additional grid points. Grouped TreeSHAP identifies recent TEC as the dominant predictive information, while residual-gain analysis shows that validation-controlled fusion adapts correction to activity conditions and retains the primary prediction when additional correction is not beneficial. These results support validation-constrained residual fusion as an effective approach for low-latitude TEC prediction.