Leveraging machine learning techniques and GPS measurements for precise TEC rate predictions

This study explores machine learning models to gain insights into dynamics of ionospheric irregularities over geodetic receivers in Mbarara (0.60° S, 30.74° E) and Kigali (1.94° S, 30.09° E). A seven-year rate of total electron content index (ROTI) database and two modeling approaches (multivariate and univariate) were employed. The motivation was to treat the database with time series techniques following a case study with and without the influence of solar wind parameters. The objective is to examine how each approach reconstructs the morphology of ROTI within 3-h time steps over a 24-h cycle. To achieve this, five machine learning models, including extreme gradient boosting (XGBoost), random forest (RF), bidirectional long-short term memory (BLSTM), unidirectional long-short term memory (LSTM) and nonlinear autoregressive with eXogenous input (NARX), were developed and evaluated. Test results demonstrate significant performance variations highlighting comparable ROTI reconstructions in the absence of the solar wind features. The RF model exhibited superior performance with the lowest mean absolute errors of 0.03 and 0.07 TECU/min and accuracies of 93% and 75% under multivariate and univariate modeling, respectively. Based on the RF model’s performance, we employed an extended database over the Ugandan (Mbar) station for further model development and validated its efficiency over a station in Rwanda (Nurk). The results provided promising insights, emphasizing the need for future research dedicated to robust and enhanced nowcasting models that leverage long-term ionospheric data, especially in regions with limited scintillation monitors.