On the possible contribution of ionospheric vertical drifts to TEC modelling in low latitudes

Habyarimana, Valence; Habarulema, John Bosco; Mungufeni, Patrick

doi:10.1016/j.asr.2020.02.005

Cited by 6 publications

(8 citation statements)

References 91 publications

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“…Usually, single station models perform better than regional models because they are easier to constrain. For example, the obtained RMSE of 5.8 TECU (percentage error ¼ 25.7%) is statistically similar to 5.7 TEC reported by Habyarimana et al (2020)…”

Section: Discussionsupporting

confidence: 83%

“…Okoh et al (2016) reported average RMSE values that are typically between 6.0 and 8.5 TECU for a neural network-based model developed over Nigeria. Interestingly, a very recent single-station study in the region (Habyarimana et al, 2020) reported an RMSE of 5.7 TECU which is very similar to the 5.8 TECU value obtained in the present study. The present study however exhibits two advantages.…”

Section: Space Weathersupporting

confidence: 91%

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Storm‐Time Modeling of the African Regional Ionospheric Total Electron Content Using Artificial Neural Networks

et al. 2020

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This paper presents the development of a storm-time total electron content (TEC) model over the African sector for the first time. The storm criterion used was |Dst| ≥ 50 nT and Kp ≥ 4. We have utilized Global Positioning System (GPS) observations from 2000 to 2018 from about 252 receivers over the African continent and surroundings within spatial coverage of 40°S-40°N latitude and 25°W-60°E longitude. To increase data coverage in areas devoid of ground-based instrumentation including oceans, we used the available radio occultation Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) TEC from 2008 to 2018. The model is based on artificial neural networks which are used to learn the relationship between TEC and the corresponding physical/geophysical input parameters representing factors which influence ionospheric variability. An important result from this effort was the inclusion of the time history of the geomagnetic activity indicators dKp dt and dDst dt which improved TEC modeling by about 5% and 12% in middle and low latitudes, respectively. Overall, the model performs comparatively well with, and sometimes better than, the earlier single station modeling efforts even during quiet conditions. Given that this is a storm-time model, this result is encouraging since it is challenging to model ionospheric parameters during geomagnetically disturbed conditions. Statistically, the average root-mean-square error (RMSE) between modeled and GPS TEC is 5.5 TECU (percentage error ¼ 30.3%) and 5.0 TECU (percentage error ¼ 30.4%) for the Southern and Northern Hemisphere midlatitudes respectively compared to 7.5 TECU (percentage error ¼ 22.0%) in low latitudes.

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Section: Discussionsupporting

confidence: 83%

Section: Space Weathersupporting

confidence: 91%

Storm‐Time Modeling of the African Regional Ionospheric Total Electron Content Using Artificial Neural Networks

et al. 2020

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“…In contrast, previous studies by Tebabal et al (2018) on a single-station feed neural network-based model over Arba Minch, Ethiopia, yielded R and RMSE values of 0.95 and 6.0 TECU, respectively, which are less favorable than those obtained in our study. Similarly, in another single-station neural network-based model over Mbarara, Uganda, by Habyarimana et al (2020), an RMSE value of 5.7 TECU was found, which exceeded the RMSE values achieved in the present study. Okoh et al (2016) reported that the RMSE values for a neural network-based model over Nigeria ranged from 5.4 to 12.6 TECU, which is significantly higher than the values obtained in our study.…”

Section: Performance Of the Modelscontrasting

confidence: 75%

“…Similarly, in another single‐station neural network‐based model over Mbarara, Uganda, by Habyarimana et al. (2020), an RMSE value of 5.7 TECU was found, which exceeded the RMSE values achieved in the present study. Okoh et al.…”

Section: Resultscontrasting

confidence: 60%

“…Despite their ability to improve several aspects of ionospheric modeling, different research findings (e.g., Habarulema et al, 2007Habarulema et al, , 2009Nigussie et al, 2013;Okoh et al, 2018;Tebabal et al, 2018) have highlighted the shortcomings of these global models in the African sector. Several neural network (NN)-based single station and regional models have been developed in the African region to fill these gaps and improve prediction accuracy (e.g., Habarulema et al, 2007Habarulema et al, , 2009Habarulema et al, , 2011Habyarimana et al, 2020;Okoh et al, 2016Okoh et al, , 2018Tebabal et al, 2018Tebabal et al, , 2019. It was found from these studies that NN-based models are very promising at capturing the overall dynamics of ionospheric variations compared to global models.…”

Section: Introductionmentioning

confidence: 99%

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

Nigusie,

Tebabal,

Galas

2024

Space Weather

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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.

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Single station modelling of ionospheric irregularities using artificial neural networks

Habyarimana,

Habarulema,

Okoh

et al. 2023

Astrophys Space Sci

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On the possible contribution of ionospheric vertical drifts to TEC modelling in low latitudes

Cited by 6 publications

References 91 publications

Storm‐Time Modeling of the African Regional Ionospheric Total Electron Content Using Artificial Neural Networks

Storm‐Time Modeling of the African Regional Ionospheric Total Electron Content Using Artificial Neural Networks

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

Single station modelling of ionospheric irregularities using artificial neural networks

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