Enhancing Geomagnetic Disturbance Predictions with Neural Networks: A Case Study on K-Index Classification
Altaibek A. Zhumabayev B. Sarsembayeva A. Nurtas M. Zakir D.
March 2025Multidisciplinary Digital Publishing Institute (MDPI)
Atmosphere
2025#16Issue 3
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
geomagnetic disturbances , geomagnetic field , K-index , neural networks
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Institute of Ionosphere, Almaty, 050020, Kazakhstan
Department of Mathematical and Computer Modeling, International Information Technology University, Almaty, 050040, Kazakhstan
Department of Theoretical and Nuclear Physics, Al-Farabi Kazakh National University, Almaty, 050040, Kazakhstan
School of Information Technology and Engineering, Kazakh-British Technical University, Almaty, 050040, Kazakhstan
Institute of Ionosphere
Department of Mathematical and Computer Modeling
Department of Theoretical and Nuclear Physics
School of Information Technology and Engineering
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