Local TEC modelling and forecasting using neural networks

Tebabal, A.; Radicella, S. M.; Nigussie, Melessew; Damtie, B.; Nava, B.; Yizengaw, E.

doi:10.1016/j.jastp.2018.03.004

Cited by 45 publications

(33 citation statements)

References 25 publications

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“…ANN had been proved able to perform better and more precisely than the IRI Model with the RMSE value of 0.4097 by using ANN and 3.8468 by using IRI Model [4]. It also provides better performance than the NeQuick 2 model for low latitude regions [30]. With that, ANN will be further investigated and analyzed on how it can improve the current ionosphere predictive model which only considers the historically observed data of ionosphere as the data input.…”

Section: Resultsmentioning

confidence: 99%

Enhancing Prediction Method of Ionosphere for Space Weather Monitoring Using Machine Learning Approaches: A Review

Salleh

Yuhaniz

Sabri

et al. 2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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This paper studies the machine learning techniques that can be used to enhance the prediction method of the ionosphere for space weather monitoring. Previously, the empirical model is used. However, there is a large deviation of the total electron content of ionosphere data for the areas located in the equatorial and low-latitude regions due to the lack of observation data contributed by these areas during the development of the empirical model. The machine learning technique is an alternative method used to develop the predictive model. Thus, in this study, the machine learning techniques that can be applied are investigated. The aim is to improve the predictive model in terms of reducing the total electron content deviation, increasing the accuracy and minimizing the error. In this review, the techniques used in previous works will be compared. The artificial neural network is found to be a suitable technique and the most favorable from the review conducted. Also, this technique can provide an accurate model for time series data and fewer errors compared to other techniques. However, due to the size and complexity of the data, the deep neural network technique that is an improved artificial neural network technique is suggested. By using this technique, an accurate ionosphere predictive model in equatorial and low region area is expected. In the future, this study will analyze further by using computing tools and real-time data.

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Section: Resultsmentioning

confidence: 99%

Enhancing Prediction Method of Ionosphere for Space Weather Monitoring Using Machine Learning Approaches: A Review

Salleh

Yuhaniz

Sabri

et al. 2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…The neuron cells of the biological nervous system take inputs, process them, and produce a response. Similarly, a neural network (NN) is an information processing mathematical structure designed for non-linear data-driven estimation (Haykin, 1999;Habarulema et al, 2007;Tebabal et al,2018). NN consists of parallel computing basic units (nodes).…”

Section: Neural Network (Nn)mentioning

confidence: 99%

“…For instance, Habarulema et al (2007Habarulema et al ( , 2009 and Tebabal et al (2018) developed a regional model by training the NN algorithm to GPS TEC obtained in South and East African sectors and compared the output of their model with the IRI-2001 and NeQuick2 standard applications, respectively and found that the regional NN model performed better than the IRI-2001 and NeQuick2. Searching model that can compute TEC faster with better performance is still the demand for the scientific community in the discipline.…”

Section: Introductionmentioning

confidence: 99%

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

Gelaw

Nigussie

Tebabal³

2022

Preprint

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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. Hosted fileessoar.10512098.1.docx available at https://authorea.com/users/524617/articles/595516comparative-assessment-of-the-performances-of-gps-tec-assisted-ntcm-nequick2-and-neuralnetwork-models-to-describe-the-east-african-equatorial-ionosphere Comparative assessment of the performances of GPS-TEC assisted NTCM, NeQuick2 and Neural Network models to describe the East-African equatorial Ionosphere

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“…Ionospheric storms, which alter depending on the solar action, the Earth's turning, and spatial, regular, monthly, and seasonal circumstances, have different impacts in the ionosphere [17,18]. e TEC values, which change over time and ought to be evaluated along with their location in space, are the principal factors for solar activity and ionosphere-magnetosphere-Sun interaction [3,[19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36]. is essay predicts the TEC values through an artificial neural network model (ANNm) [7,8,[37][38][39][40][41][42][43][44][45] over the superstorms of November 20, 2003 (Dst � -422 nT) and November 08, 2004 (Dst � -374 nT).…”

Section: Introductionmentioning

confidence: 99%

Discussing Total Electron Content over the Solar Wind Parameters

Eroğlu

2022

Mathematical Problems in Engineering

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Modeling and forecasting of Total Electron Content (TEC) values by an Artificial Neural Network model (ANNm) have high agreement on November 2003, 2004 superstorms. The work discusses Solar Wind Parameters (SWp) from OMNI (Operating Missions as a Node on the Internet) and TEC (TECU) data (International Reference Ionosphere) IRI-2012, IRI-2016 on November 20, 2003 (Dst = –422 nT) and on November 08, 2004 (Dst = –374 nT) Geomagnetic Storms (GSs). The paper commences with a 120-hour GS exhibition of SWp and proceeds with the correlation data of the variables, their hierarchical tracks, and inner dispersions. The ANNm with SWp as the input and TEC data as the output are introduced. The performance of the ANNm for 2003 and 2004 superstorms is adequate. The Correlation Coefficient (R) and Root Mean Square Error (RMSE) of the ANNm are 97.5%, 1.17 TECU (IRI-2012), and 97.9%, 1.09 TECU (IRI-2016) for the 2003 GS and 97.0%, 0.89 TECU (IRI-2012), and 98.0%, 1.61 TECU (IRI-2016) for 2004 GS. Parameters effect of the R constant of TEC data points out to the dynamic pressure (nPa), the magnetic field Bz component (nT), the flow speed (km/s), and the proton density (1/cm3). Besides, the absolute total error and the variance of the predicted TEC data for November 2003 and November 2004 GSs are 0.06 (0.30%) with 0.013 variance (IRI-2012), 0.09 (0.49%) with 0.016 variance (IRI-2016) for 2003 storm and 0.13 (0.73%) with 0.033 variance (IRI-2012), and 0.11 (1.06%) with 0.035 variance (IRI-2016) for 2004. It means that the paper models TEC data with considerable consistency over the SWp.

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Local TEC modelling and forecasting using neural networks

Cited by 45 publications

References 25 publications

Enhancing Prediction Method of Ionosphere for Space Weather Monitoring Using Machine Learning Approaches: A Review

Enhancing Prediction Method of Ionosphere for Space Weather Monitoring Using Machine Learning Approaches: A Review

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

Discussing Total Electron Content over the Solar Wind Parameters

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