Impact of Solar and Geomagnetic Driver Selection on 24 h-Ahead Global VTEC Prediction in a Deep Learning Framework: A ConvLSTM Case Study

This study investigates how solar and geomagnetic driver selection affects 24 h-ahead global ionospheric vertical total electron content (VTEC) prediction under different geomagnetic conditions. A four-step feature selection strategy involving importance evaluation, redundancy elimination, physical interpretability prioritization, and performance validation was developed to identify five key drivers from candidate solar and geomagnetic factors. Using global ionospheric maps provided by the Center for Orbit Determination in Europe (CODE) from 2014 to 2018, a non-overlapping 90-day temporal block scheme was adopted to reduce the risk of temporal information leakage. Six ablation experiments were conducted to compare the predictive performance of different driver combinations. The results show that the full-factor configuration selected by the proposed strategy achieved the most favorable overall performance among the tested combinations, although the global-average improvement relative to the baseline remained modest. The optimal driver combination varied with geomagnetic disturbance level, and the contribution of external drivers showed clear latitudinal dependence. In addition, the full-factor configuration yielded a more balanced global error distribution and was associated with slower error accumulation over the 24 h horizon. These findings suggest that physically guided driver selection is useful for constructing more physically meaningful driver combinations and for improving long-horizon prediction stability within a unified ConvLSTM-based framework.