Modeling Ionospheric TEC Using Gradient Boosting Based and Stacking Machine Learning Techniques

• Published in: Space weather Vol. 22; no. 3
• Main Authors: Nigusie, Ayanew, Tebabal, Ambelu, Galas, Roman
• Format: Journal Article
• Language: English
• Published: 01.03.2024
• Subjects: gradient boosting machine; LightGBM; machine learning; stacking; total electron content; XGBoost
• ISSN: 1542-7390; 1542-7390
• DOI: 10.1029/2023SW003821

Accurately predicting and modeling the ionospheric total electron content (TEC) can greatly improve the accuracy of satellite navigation and positioning and help to correct ionospheric delay. This study assesses the effectiveness of four different machine learning (ML) models in predicting hourly vertical TEC (VTEC) data for a single‐station study over Ethiopia. The models employed include gradient boosting machine (GBM), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM) algorithms, and a stacked combination of these algorithms with a linear regression algorithm. The models relied on input variables that represent solar activity, geomagnetic activity, season, time of the day, interplanetary magnetic field, and solar wind. The models were trained using the VTEC data from January 2011 to December 2018, excluding the testing data. The testing data comprised the data for the year 2015 and the initial 6 months of 2017. The RandomizedSearchCV algorithm was used to determine the optimal hyperparameters of the models. The predicted VTEC values of the four ML models were strongly correlated with the GPS VTEC, with a correlation coefficient of ∼0.96, which is significantly higher than the corresponding value of the International Reference Ionosphere (IRI 2020) model, which is 0.87. Comparing the GPS VTEC values with the predicted VTEC values based on diurnal and seasonal characteristics showed that the predictions of the developed models were generally in good agreement and outperformed the IRI 2020 model. Overall, the ML models used in this study demonstrated promising potential for accurate single‐station VTEC prediction over Ethiopia. Plain Language Summary Studying the ionosphere is crucial as it significantly impacts satellite navigation and communication systems. However, a major challenge in ionospheric studies is the unavailability of observational total electron content data in some regions. To tackle this problem, researchers have employed machine learning (ML) modeling as a solution. In this study, we used ML algorithms such as gradient boosting machine, XGBoost, and LightGBM, as well as their stacked integration along with the linear regression algorithm, to model the ionospheric vertical total electron content over a single GPS receiver station in the low‐latitude ionospheric region. The methods employed are highly efficient in terms of computational resources. Key Points Three gradient‐boosting decision tree‐based algorithms and their stacked combination with the linear regression algorithm are applied The predictions of the machine learning (ML) models are strongly correlated with the GPS VTEC, with a correlation coefficient of ∼0.96 The ML models utilized in this work significantly outperformed the International Reference Ionosphere (IRI 2020) global model