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\begin{document}

\title{Modeling ionospheric TEC using gradient boosting based and stacking
machine learning techniques}



\author[1]{Ayanew Shiferaw}%
\author[2]{Ambelu Tababal}%
\author[3]{Roman Galas}%
\affil[1]{Oda Bultum University}%
\affil[2]{Bahir Dar University}%
\affil[3]{Technical University of Berlin}%


\vspace{-1em}



  
  \date{May 22, 2024}


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\begin{abstract}
Accurately predicting and modeling the total electron content (TEC) of
the ionosphere can greatly improve the accuracy of satellite navigation
and positioning and help to correct ionospheric delay. This study tested
the effectiveness of four different machine learning (ML) models in
predicting hourly vertical TEC (VTEC) data from a single station in
Addis Ababa, Ethiopia. The models used were 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 six 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 \$\textbackslash{}sim\$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 GBDT-based algorithms and their stacked integration
demonstrated promising potential for predicting VTEC over Addis Ababa,
Ethiopia.%
\end{abstract}%



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