AI for Space Weather Prediction: Protecting Satellites, Communication Networks and Critical Infrastructure

Ryuho Kataoka

AI for Space Weather Prediction: Protecting Satellites, Communication Networks and Critical Infrastructure

Author(s)	Prof. Ryuho Kataoka
Country	United States
Abstract	Space-weather events produced by solar flares, coronal mass ejections, solar energetic particles and geomagnetic disturbances can interfere with satellites, navigation services, radio communication and terrestrial power infrastructure. Conventional forecasting systems combine solar observations, numerical models and expert interpretation, but their operational effectiveness is restricted by nonlinear physical interactions, incomplete measurements and the rapid evolution of severe events. Artificial intelligence offers an additional forecasting layer capable of learning spatiotemporal relationships from solar imagery, magnetograms, solar-wind measurements and historical geomagnetic indices. This study develops a conceptual architecture for an artificial-intelligence-enabled space-weather prediction and infrastructure-protection platform. The proposed architecture integrates multimodal observations, machine-learning models, physics-based simulations, uncertainty estimation and infrastructure-specific decision rules. A simulation-based numerical assessment compares physics-only forecasting, conventional machine learning and hybrid physics-informed AI at warning horizons ranging from 15 to 90 minutes. The illustrative results indicate that the hybrid approach maintains higher composite forecast skill across every evaluated horizon. At 60 minutes, for example, simulated forecast skill reaches 80%, compared with 71% for conventional machine learning and 64% for the physics-only baseline. The findings suggest that the greatest value of AI is not merely improved event classification. Its strategic contribution lies in converting uncertain scientific forecasts into timely protective actions, such as satellite safe-mode initiation, communication-channel switching, GNSS integrity alerts and power-grid load management. Operational deployment nevertheless requires calibrated uncertainty, interpretable outputs, data-quality governance, cybersecurity controls and continuous human supervision.
Keywords	artificial intelligence, space weather, geomagnetic storms, solar flares, satellite protection, communication networks, critical infrastructure, physics-informed machine learning
Field	Engineering
Published In	Volume 8, Issue 2, March-April 2026
Published On	2026-03-23

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American Journal of Advanced Multidisciplinary Research and Innovation

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AI for Space Weather Prediction: Protecting Satellites, Communication Networks and Critical Infrastructure

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