Comparative evaluation of neural and physics-based models for forecasting ionospheric Total Electron Content at Low and Mid-latitude Stations during maximum and minimum phases of Solar Cycle 24.
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| Title: | Comparative evaluation of neural and physics-based models for forecasting ionospheric Total Electron Content at Low and Mid-latitude Stations during maximum and minimum phases of Solar Cycle 24. |
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| Authors: | Chaurasiya, Sunil Kumar1 (AUTHOR), Patel, Kalpana2 (AUTHOR), Singh, Abhay Kumar3 (AUTHOR) singhak@bhu.ac.in |
| Source: | Astrophysics & Space Science. May2026, Vol. 371 Issue 5, p1-19. 19p. |
| Subjects: | Long short-term memory, Solar cycle, General circulation model, Scientific models, Space environment, Ionospheric electron density, Artificial neural networks |
| Geographic Terms: | Novosibirsk (Russia), Varanasi (Uttar Pradesh, India), Port Blair (India) |
| Abstract: | Forecasting ionospheric Total Electron Content (TEC) remains a challenging task for space weather applications due to strong variability across latitudes and solar cycle phases. This study presents a comparative analysis of neural network models, namely Long Short-Term Memory (LSTM) and Backpropagation (BP), and physics-based models, including the Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIE-GCM) and NeQuick. The evaluation is performed at three GPS stations: Novosibirsk (mid-latitude), Varanasi (low-latitude), and Port Blair (equatorial latitude), during the solar maximum year of 2014 and the solar minimum year of 2018 during Solar Cycle 24. The results reveal station- and solar cycle phase-dependent variations in model performance. In Novosibirsk, LSTM consistently achieved the highest correlation (R ≈ 0.85) and the lowest RMSE, particularly during the solar maximum phase, successfully capturing the variability of storm-time TEC. Varanasi exhibited strong seasonal variability, with neural models maintaining stable performance across both solar phases, whereas physics-based models struggled with equatorial anomaly effects, particularly during the solar minimum. Port Blair showed the largest model differences: LSTM maintained stable performance with reduced error distributions, while NeQuick consistently underestimated TEC, with errors increasing during the solar minimum phase. Seasonal analysis indicates that neural approaches maintain stable performance across all seasons, whereas physics-based models degrade during transitional seasons. At local time 14:00, comparative analysis shows that LSTM exhibits lower RMSE across all stations and phases, achieving a relative accuracy share greater than 40%, while physics-based models remain below 20% accuracy share. All statistical metrics, including the correlation coefficient (R), RMSE, and MRE, were computed by averaging results over stations, seasons, and solar cycle phases under both quiet and disturbed conditions. This station- and phase-resolved study demonstrates the effectiveness of deep learning architectures in ionospheric forecasting. The study enhances TEC prediction by demonstrating improved performance across diverse latitudinal regimes and solar cycle phases, providing comparative insights for space-weather forecasting. [ABSTRACT FROM AUTHOR] |
| Copyright of Astrophysics & Space Science is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 194698621 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Comparative evaluation of neural and physics-based models for forecasting ionospheric Total Electron Content at Low and Mid-latitude Stations during maximum and minimum phases of Solar Cycle 24. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Chaurasiya%2C+Sunil+Kumar%22">Chaurasiya, Sunil Kumar</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Patel%2C+Kalpana%22">Patel, Kalpana</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Singh%2C+Abhay+Kumar%22">Singh, Abhay Kumar</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> singhak@bhu.ac.in</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Astrophysics+%26+Space+Science%22">Astrophysics & Space Science</searchLink>. May2026, Vol. 371 Issue 5, p1-19. 19p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Long+short-term+memory%22">Long short-term memory</searchLink><br /><searchLink fieldCode="DE" term="%22Solar+cycle%22">Solar cycle</searchLink><br /><searchLink fieldCode="DE" term="%22General+circulation+model%22">General circulation model</searchLink><br /><searchLink fieldCode="DE" term="%22Scientific+models%22">Scientific models</searchLink><br /><searchLink fieldCode="DE" term="%22Space+environment%22">Space environment</searchLink><br /><searchLink fieldCode="DE" term="%22Ionospheric+electron+density%22">Ionospheric electron density</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Novosibirsk+%28Russia%29%22">Novosibirsk (Russia)</searchLink><br /><searchLink fieldCode="DE" term="%22Varanasi+%28Uttar+Pradesh%2C+India%29%22">Varanasi (Uttar Pradesh, India)</searchLink><br /><searchLink fieldCode="DE" term="%22Port+Blair+%28India%29%22">Port Blair (India)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Forecasting ionospheric Total Electron Content (TEC) remains a challenging task for space weather applications due to strong variability across latitudes and solar cycle phases. This study presents a comparative analysis of neural network models, namely Long Short-Term Memory (LSTM) and Backpropagation (BP), and physics-based models, including the Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIE-GCM) and NeQuick. The evaluation is performed at three GPS stations: Novosibirsk (mid-latitude), Varanasi (low-latitude), and Port Blair (equatorial latitude), during the solar maximum year of 2014 and the solar minimum year of 2018 during Solar Cycle 24. The results reveal station- and solar cycle phase-dependent variations in model performance. In Novosibirsk, LSTM consistently achieved the highest correlation (R ≈ 0.85) and the lowest RMSE, particularly during the solar maximum phase, successfully capturing the variability of storm-time TEC. Varanasi exhibited strong seasonal variability, with neural models maintaining stable performance across both solar phases, whereas physics-based models struggled with equatorial anomaly effects, particularly during the solar minimum. Port Blair showed the largest model differences: LSTM maintained stable performance with reduced error distributions, while NeQuick consistently underestimated TEC, with errors increasing during the solar minimum phase. Seasonal analysis indicates that neural approaches maintain stable performance across all seasons, whereas physics-based models degrade during transitional seasons. At local time 14:00, comparative analysis shows that LSTM exhibits lower RMSE across all stations and phases, achieving a relative accuracy share greater than 40%, while physics-based models remain below 20% accuracy share. All statistical metrics, including the correlation coefficient (R), RMSE, and MRE, were computed by averaging results over stations, seasons, and solar cycle phases under both quiet and disturbed conditions. This station- and phase-resolved study demonstrates the effectiveness of deep learning architectures in ionospheric forecasting. The study enhances TEC prediction by demonstrating improved performance across diverse latitudinal regimes and solar cycle phases, providing comparative insights for space-weather forecasting. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Astrophysics & Space Science is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10509-026-04591-3 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 19 StartPage: 1 Subjects: – SubjectFull: Long short-term memory Type: general – SubjectFull: Solar cycle Type: general – SubjectFull: General circulation model Type: general – SubjectFull: Scientific models Type: general – SubjectFull: Space environment Type: general – SubjectFull: Ionospheric electron density Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Novosibirsk (Russia) Type: general – SubjectFull: Varanasi (Uttar Pradesh, India) Type: general – SubjectFull: Port Blair (India) Type: general Titles: – TitleFull: Comparative evaluation of neural and physics-based models for forecasting ionospheric Total Electron Content at Low and Mid-latitude Stations during maximum and minimum phases of Solar Cycle 24. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Chaurasiya, Sunil Kumar – PersonEntity: Name: NameFull: Patel, Kalpana – PersonEntity: Name: NameFull: Singh, Abhay Kumar IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 0004640X Numbering: – Type: volume Value: 371 – Type: issue Value: 5 Titles: – TitleFull: Astrophysics & Space Science Type: main |
| ResultId | 1 |
