Enhancing Geomagnetic Disturbance Predictions with Neural Networks: A Case Study on K-Index Classification
To explore the application of neural networks for estimating geomagnetic field disturbances, this study pays particular attention to K-index classification. The primary goal is to develop a robust and efficient method for classifying different levels of geomagnetic activity using neural networks. Ou...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/16/3/267 |
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| author | Aizhan Altaibek Beibit Zhumabayev Aiganym Sarsembayeva Marat Nurtas Diana Zakir |
| author_facet | Aizhan Altaibek Beibit Zhumabayev Aiganym Sarsembayeva Marat Nurtas Diana Zakir |
| author_sort | Aizhan Altaibek |
| collection | DOAJ |
| description | To explore the application of neural networks for estimating geomagnetic field disturbances, this study pays particular attention to K-index classification. The primary goal is to develop a robust and efficient method for classifying different levels of geomagnetic activity using neural networks. Our work encompasses data preprocessing, model architecture optimization, and a thorough evaluation of classification performance. A new neural-network approach is proposed to address the specific complexities of geomagnetic data, and its merits are compared with those of conventional techniques. Notably, Long Short-Term Memory (LSTM) models significantly outperformed traditional methods, achieving up to 98% classification accuracy. These findings demonstrate that neural networks can be effectively applied in geomagnetic studies, supporting AI-based forecasting and enabling further integration into space weather research |
| format | Article |
| id | doaj-art-d05821dac4e54e3391611535b913c6f2 |
| institution | DOAJ |
| issn | 2073-4433 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj-art-d05821dac4e54e3391611535b913c6f22025-08-20T02:42:38ZengMDPI AGAtmosphere2073-44332025-02-0116326710.3390/atmos16030267Enhancing Geomagnetic Disturbance Predictions with Neural Networks: A Case Study on K-Index ClassificationAizhan Altaibek0Beibit Zhumabayev1Aiganym Sarsembayeva2Marat Nurtas3Diana Zakir4Institute of Ionosphere, Almaty 050020, KazakhstanInstitute of Ionosphere, Almaty 050020, KazakhstanDepartment of Theoretical and Nuclear Physics, Al-Farabi Kazakh National University, Almaty 050040, KazakhstanInstitute of Ionosphere, Almaty 050020, KazakhstanInstitute of Ionosphere, Almaty 050020, KazakhstanTo explore the application of neural networks for estimating geomagnetic field disturbances, this study pays particular attention to K-index classification. The primary goal is to develop a robust and efficient method for classifying different levels of geomagnetic activity using neural networks. Our work encompasses data preprocessing, model architecture optimization, and a thorough evaluation of classification performance. A new neural-network approach is proposed to address the specific complexities of geomagnetic data, and its merits are compared with those of conventional techniques. Notably, Long Short-Term Memory (LSTM) models significantly outperformed traditional methods, achieving up to 98% classification accuracy. These findings demonstrate that neural networks can be effectively applied in geomagnetic studies, supporting AI-based forecasting and enabling further integration into space weather researchhttps://www.mdpi.com/2073-4433/16/3/267geomagnetic fieldgeomagnetic disturbancesneural networksK-index |
| spellingShingle | Aizhan Altaibek Beibit Zhumabayev Aiganym Sarsembayeva Marat Nurtas Diana Zakir Enhancing Geomagnetic Disturbance Predictions with Neural Networks: A Case Study on K-Index Classification Atmosphere geomagnetic field geomagnetic disturbances neural networks K-index |
| title | Enhancing Geomagnetic Disturbance Predictions with Neural Networks: A Case Study on K-Index Classification |
| title_full | Enhancing Geomagnetic Disturbance Predictions with Neural Networks: A Case Study on K-Index Classification |
| title_fullStr | Enhancing Geomagnetic Disturbance Predictions with Neural Networks: A Case Study on K-Index Classification |
| title_full_unstemmed | Enhancing Geomagnetic Disturbance Predictions with Neural Networks: A Case Study on K-Index Classification |
| title_short | Enhancing Geomagnetic Disturbance Predictions with Neural Networks: A Case Study on K-Index Classification |
| title_sort | enhancing geomagnetic disturbance predictions with neural networks a case study on k index classification |
| topic | geomagnetic field geomagnetic disturbances neural networks K-index |
| url | https://www.mdpi.com/2073-4433/16/3/267 |
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