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

Okoh, Daniel; Habarulema, John Bosco; Rabiu, Babatunde; Seemala, G. K.; Joshua, B.W.; Olwendo, Joseph; Obrou, O. K.; Matamba, Tshimangadzo Merline

doi:10.1029/2020sw002525

Cited by 28 publications

(17 citation statements)

References 48 publications

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“…In this study, we also introduce two magnetic disturbance indices to help predict the ionosphere VTEC in the model training phase. The geomagnetic storm is empirically defined as Dst <= -50 nT and Kp >= 4 in this study [45]. Different from solar activities, a geomagnetic storm is a shortterm event, which typically lasts a few hours to several days.…”

Section: B Performance Comparison Evaluation In High Solar Activity Periodmentioning

confidence: 99%

Machine Learning-Based Short-Term GPS TEC Forecasting During High Solar Activity and Magnetic Storm Periods

Han

Wang

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Precise ionospheric total electron content (TEC) is critical for many aerospace applications, and forecasting ionospheric TEC is of great significance to it. Besides, short-term prediction of TEC values fills the gap between the TEC product latency and the precision. The machine learning-based approaches are promising in solving the non-linear prediction issues, particularly suitable for short-term GPS TEC forecasting due to its complex temporal and spatial variation. In this paper, four different machine learning models, i.e., Artificial Neural Network (ANN), Long Short-Term Memory (LSTM) networks, Adaptive Neuro-Fuzzy Inference System based on Subtractive Clustering (ANFIS-SC), and Gradient Boosting Decision Tree (GBDT) are applied for forecasting ionospheric TEC in three IGS GNSS monitoring stations at the low-latitude region (16°S to 10°S). The performance of these approaches in extreme conditions is investigated, including the high solar activity and magnetic storm, which are the most challenging scenario for TEC prediction. The results show that the machine learning algorithms outperform the Global Ionospheric Map (GIM) prediction model. The prediction accuracy during the high solar activity period was improved from 37.93% to 49.28%. During the magnetic storm period, the prediction accuracy was improved from 28.16% to 67.39% . Among the machine learning algorithms, the GBDT model outperforms the rest three algorithms in ionosphere prediction scenarios, which improves the prediction accuracy by 5.6% and 12.7% than the rest three approaches on average during high solar activity (2012-2015) and magnetic storm periods respectively.

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Section: B Performance Comparison Evaluation In High Solar Activity Periodmentioning

confidence: 99%

Machine Learning-Based Short-Term GPS TEC Forecasting During High Solar Activity and Magnetic Storm Periods

Han

Wang

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…The TEC values extracted from the NRT TEC maps were compared with those of ionosonde and AfriTEC model (Okoh et al, 2019(Okoh et al, , 2020 over Hermanus and Grahamstown. The TEC values (NRT TEC est) are interpolated from the median filtered gridded data at the coordinates of the ionosondes (Hermanus (HE13N) and Grahamstown (GR13L) for this evaluation).…”

Section: Evaluation Of Tecmentioning

confidence: 99%

“…Further, an evaluation period from May to November 2021 is presented and discussed with the products' errors and quality factors. The TEC values from Tmv2 are compared with those from ionosondes and the AFriTEC model (Okoh et al, 2019(Okoh et al, , 2020. The method to determine both temporal and spatial gradients are presented and discussed for the 4-5 November 2021 negative ionospheric storm.…”

Section: Introductionmentioning

confidence: 99%

Development and Evaluation of Near‐Real Time TEC and Ancillary Products for SANSA Space Weather

Matamba

Danskin

2022

Space Weather

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In this paper, the second version of SANSA's Total Electron Content (TEC) maps is presented with its associated methodology and assumptions. The method is based on Ma and Maruyama (2003, https://doi.org/10.5194/angeo-21-2083-2003) determination of receiver bias and hence TEC for individual receivers. By combining values of slant TEC from 20 South African Trignet stations, maps of TEC, spatial gradient of TEC and temporal gradient of TEC can be determined. Quality factors and error of fit are determined for each map and are analyzed to identify the level of validity for TEC maps. In addition, the first operational estimation of TEC gradients both temporal and spatial are presented and discussed for the 4–5 November 2021 negative ionospheric storm. The TEC from the maps shows good agreement with the ionosonde TEC and AfriTEC model.

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“…Deep learning methods have been widely used in the prediction of the ionospheric TEC for a single station (Huang et al., 2015; Kaselimi et al., 2020; Ruwali et al., 2021; Srivani et al., 2019; Xiong et al., 2021; Zewdie et al., 2021), for a region (Li et al., 2020; Okoh et al., 2016, 2019, 2020; Razin et al., 2015; Sabzehee et al., 2018; Song et al., 2018; Tebabal et al., 2019; Uwamahoro et al., 2018), and for the globe (Cesaroni et al., 2020; Chen et al., 2019; Liu et al., 2020; Zhukov et al., 2020). TEC prediction models for a single station based on the artificial neural network (ANN) method (Huang et al., 2015; Huang & Yuan, 2014), the long short‐term memory (LSTM) method (Kaselimi et al., 2020; Srivani et al., 2019; Xiong et al., 2021; Zewdie et al., 2021), and the hybrid deep learning method (Ruwali et al., 2021), all have recently shown promising results.…”

Section: Introductionmentioning

confidence: 99%

An Investigation of Ionospheric TEC Prediction Maps Over China Using Bidirectional Long Short‐Term Memory Method

Shi

Zhang

et al. 2022

Space Weather

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The ionospheric total electron content (TEC) is an important ionospheric parameter, and it is widely utilized in research such as space weather prediction and precise positioning. However, it is still challenging to develop an ionospheric TEC prediction model with high accuracy. In this study, a new ionospheric TEC model over China was developed using the bidirectional long short‐term memory (bi‐LSTM) method and observations from 257 ground‐based global navigation satellite system (GNSS) stations in the Crustal Movement Observation Network of China from January 2018 to December 2021. The root mean square errors of the bi‐LSTM‐based model’s 1 and 2 hr ahead predictions on the test data set (from June 2021 to December 2021) are 1.12 and 1.68 TECU, respectively, which are 75/50/32% and 72/48/22% smaller than those of the IRI‐2016, artificial neural network and LSTM‐based models, correspondingly. The bi‐LSTM‐based model shows the best performance, which is most likely due to the fact that the sequence information in both forward and backward directions is taken into consideration in the new model. In addition, the diurnal variation, seasonal variation of the ionospheric TEC, and variations under geomagnetic storm conditions are successfully captured by the bi‐LSTM‐based model. Moreover, the TEC maps resulting from the bi‐LSTM model agree well with those obtained from the final ionospheric product from the Chinese Academy of Sciences. Hence, the new model can be a good choice for the investigation of the spatiotemporal variation trend in the ionosphere and GNSS navigation.

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

Cited by 28 publications

References 48 publications

Machine Learning-Based Short-Term GPS TEC Forecasting During High Solar Activity and Magnetic Storm Periods

Machine Learning-Based Short-Term GPS TEC Forecasting During High Solar Activity and Magnetic Storm Periods

Development and Evaluation of Near‐Real Time TEC and Ancillary Products for SANSA Space Weather

An Investigation of Ionospheric TEC Prediction Maps Over China Using Bidirectional Long Short‐Term Memory Method

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