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\begin{document}

\title{Comparative assessment of the performances of GPS-TEC assisted NTCM,
NeQuick2 and Neural Network models to describe the East-African
equatorial Ionosphere}



\author[1]{Balew Getahun Gelaw}%
\author[1]{Melessew Nigussie}%
\author[2]{Ambelu Tebabal}%
\affil[1]{Bahir Dar University}%
\affil[2]{Unknown}%


\vspace{-1em}



  
  \date{May 29, 2024}


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\begin{abstract}
Different ionospheric climatological models such as NeQuick2 and NTCM
have been developed to mitigate the ionosphere impact on the
trans-ionosphere propagating radio wave. Moreover, the Neural Network
(NN) is used to model and characterize the ionosphere. However, no one
has compared the performances of NeQuick2, NTCM, and NN after adapting
to GPS TEC. This study evaluates their performances in the East-African
region in 2013 and 2015. It has been done by computing their drivers
(effective ionization level, Az for NeQuick2 and ionization driving
index, Id for NTCM) through least-square fitting to TEC observation
sense. NN-algorithm has also been trained and tested with observed TEC
used for NeQuick2 and NTCM adaptation. The annual performance test has
shown that the correlation coefficient (R) values between observed and
NTCM modeled TEC, after an adaption, are better than the corresponding
values obtained from NeQuick2 and NN. It also shows that the standard
deviations (STD) and root-mean-square errors (RMSE) obtained for NTCM
are smaller than the STD and RMSE computed for NeQuick2 and NN. On the
other hand, the daily performance test of now-casting and predicting
showed that the NN performs the best, followed by NTCM. However, the
1-hour prediction test showed that NTCM performs the best among the
models considered in this study.%
\end{abstract}%



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