Geometric deep learning for ionospheric TEC modeling using a temporal graph convolutional network

Abstract This document proposes a spatiotemporal deep learning model for ionospheric total electron content (TEC) modeling using global navigation satellite systems (GNSSs) observables. Data from dual-frequency GNSS receivers are used to compute the daily GNSS TEC timeseries. Usually, these timeseries are computed independently per GNSS permanent station, and the state-of-the art models proposed in literature exploit temporal characteristics of the timeseries and neglect any spatial dependencies and information from different stations. In our approach, we propose a practical solution for parallel processing of TEC timeseries and additional indicators from various adjacent stations to predict future VTEC values. We face the problem in both spatial and temporal dimensions adopting a graph neural network-based approach from the broader family of geometric deep learning. According to our proposed scheme, the different adjacent GNSS stations are structured in a graph and then, we apply the proposed temporal graph convolutional network called ION_TGNN. Our model predicts future vertical TEC (VTEC) values for all stations in a single run with mae error better than 1.0 TECU. Comparisons with state-of-the art models show the superiority of the proposed method in terms of performance but also in terms of computational cost during training and test phases.