Space Weather research in the Digital Age and across the full data lifecycle: Introduction to the Topical Issue

McGranaghan, Ryan; Camporeale, Enrico; Georgoulis, Manolis K.; Anastasiadis, A.

doi:10.1051/swsc/2021037

Cited by 12 publications

(2 citation statements)

References 16 publications

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“…The study of the effects of space weather on the environment is one of the prioritized areas of geophysics [1]. One of the components of the environment is the near outer space of the Earth.…”

Section: Introductionmentioning

confidence: 99%

Comparison of the Forecast Accuracy of Total Electron Content for Bidirectional and Temporal Convolutional Neural Networks in European Region

Kharakhashyan

Maltseva

2023

Remote Sensing

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Machine learning can play a significant role in bringing new insights in GNSS remote sensing for ionosphere monitoring and modeling to service. In this paper, a set of multilayer architectures of neural networks is proposed and considered, including both neural networks based on LSTM and GRU, and temporal convolutional networks. The set of methods included 10 architectures: TCN, modified LSTM-/GRU-based deep networks, including bidirectional ones, and BiTCN. The comparison of TEC forecasting accuracy is performed between individual architectures, as well as their bidirectional modifications, by means of MAE, MAPE, and RMSE estimates. The F10.7, 10 Kp, Np, Vsw, and Dst indices are used as predictors. The results are presented for the reference station Juliusruh, three stations along the meridian 30°E (Murmansk, Moscow, and Nicosia), and three years of different levels of solar activity (2015, 2020, and 2022). The MAE and RMSE values depend on the station latitude, following the solar activity. The conventional LSTM and GRU networks with the proposed modifications and the TCN provide results at the same level of accuracy. The use of bidirectional neural networks significantly improves forecast accuracy for all the architectures and all stations. The best results are provided by the BiTCN architecture, with MAE values less than 0.3 TECU, RMSE less than 0.6 TECU, and MAPE less than 5%.

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Section: Introductionmentioning

confidence: 99%

Comparison of the Forecast Accuracy of Total Electron Content for Bidirectional and Temporal Convolutional Neural Networks in European Region

Kharakhashyan

Maltseva

2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…The study of the effects of space weather on the environment is one of the priority areas of geophysics [1]. One of the components of the environment is the near outer space of the Earth.…”

Section: Introductionmentioning

confidence: 99%

Comparison of the Forecast Accuracy of Total Electron Content for Bidirectional and Temporal Convolutional Neural Networks in European Region

Kharakhashyan,

Maltseva

2023

Prime Archives in Remote Sensing

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Machine learning can play a significant role in bringing new insights in GNSS remote sensing for ionosphere monitoring and modeling to service. In this paper, a set of multilayer architectures of neural networks is proposed and considered, including both neural networks based on LSTM and GRU, and Temporal Convolutional Networks. The set of methods included 10 architectures: TCN, modified LSTM/GRU-based deep networks, including bidirectional ones, and BiTCN. The comparison of TEC forecasting accuracy is performed between individual architectures, as well as their bidirectional modifications, by means of MAE, MAPE and RMSE estimates. www.videleaf.com The F10.7, 10Kp, Np, Vsw, Dst indices are used as predictors. The results are presented for the reference station Juliusruh, three stations along the meridian 30° E (Murmansk, Moscow, Nicosia), and three years of different levels of solar activity (2015, 2020, 2022). The MAE and RMSE values depend on the station latitude, following the solar activity. The conventional LSTM and GRU networks with the proposed modifications and the TCN provide the results at the same level of accuracy. The use of bidirectional neural networks significantly improves forecast accuracy for all the architectures and all stations. The best results are provided by the BiTCN architecture, with MAE values less than 0.3 TECU, RMSE less than 0.6 TECU, MAPE less than 5%.

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Use of Classification Algorithms to Predict the Grade of Geomagnetic Disturbance

Gadzhiev

Myagkova²,

Доленко³

2022

Studies in Computational Intelligence

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Space Weather research in the Digital Age and across the full data lifecycle: Introduction to the Topical Issue

Cited by 12 publications

References 16 publications

Comparison of the Forecast Accuracy of Total Electron Content for Bidirectional and Temporal Convolutional Neural Networks in European Region

Comparison of the Forecast Accuracy of Total Electron Content for Bidirectional and Temporal Convolutional Neural Networks in European Region

Comparison of the Forecast Accuracy of Total Electron Content for Bidirectional and Temporal Convolutional Neural Networks in European Region

Use of Classification Algorithms to Predict the Grade of Geomagnetic Disturbance

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