A machine learning model for vertical total electron content forecasting and global navigation satellite system ionospheric delay estimation across selected African stations.

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dc.contributor.authorNantale, Tracy Cynthia
dc.date.accessioned2026-09-21T13:22:36Z
dc.date.available2026-09-21T13:22:36Z
dc.date.issued2026
dc.descriptionDissertation
dc.description.abstractGlobal Navigation Satellite System (GNSS) users in equatorial and low-latitude Africa experience substantial ionospheric delay, yet few regional tools link forecasting to practical delay estimation. This study developed and evaluated a machine-learning-based tool for 24-hour Vertical Total Electron Content (VTEC) forecasting and GNSS ionospheric delay estimation across a network of 31 selected African GNSS stations, using a regional multi-station data set covering the years from 2000 to 2025. Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) architectures were compared across full-feature, exogenous-only, and VTEC-only input configurations. Recent VTEC history was far more informative than the space-weather inputs alone (validation RMSE of approximately 1.91 TECU for theVTEC-onlyGRUversus2.33TECUfor the exogenous-only GRU), and the lighter VTEC-only models matched the full-feature models while needing fewer inputs. A 48-hour lookback window was retained as the preferred operational compromise, and the VTEC-only GRU was selected over the tuned LSTM under a predefined validation criterion, the two performing similarly rather than one being clearly superior. On 446,738 chronological test sequences (10,721,712 forecast-target pairs across the 24 output horizons), the final GRU achieved a Root Mean Square Error (RMSE) of 3.9108 TEC Units (TECU), a Mean Absolute Error (MAE) of 2.5523 TECU, an 𝑅2 of 0.9456, and a correlation coefficient of 0.9724, improving on previous-day persistence by 17.0% and on the International Reference Ionosphere 2020 (IRI-2020) by 48.0%. This advantage held across all forecast horizons and ionospheric conditions, though errors rose when the ionosphere was disturbed, corresponding to a GPS L1 vertical delay-error equivalent of 0.6350 m. The selected GRU was integrated into a prototype that uses the latest 48 hours of station specific VTEC to generate 24-hour VTEC and delay forecasts, providing a foundation for future regional GNSS ionospheric monitoring and delay-estimation services in Africa.
dc.description.sponsorshipAssoc. Prof. Gilbert Gilibrays Ocen ; Dr. Godfrey Kibalya Mirondo ; Busitema University
dc.identifier.citationNantale, T. C. (2026). A machine learning model for vertical total electron content forecasting and global navigation satellite system ionospheric delay estimation across selected African stations. [Unpublished dissertation]. Busitema University.
dc.identifier.urihttps://bdears.busitema.ac.ug/handle/123456789/9604
dc.language.isoen
dc.publisherBusitema University
dc.titleA machine learning model for vertical total electron content forecasting and global navigation satellite system ionospheric delay estimation across selected African stations.
dc.typeOther
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