Machine Learning-Based Comprehensive Space Weather Monitoring and Prediction System

Space weather events, driven by solar phenomena such as solar flares, coronal mass ejections (CMEs), and solar winds, present critical challenges to both space-based and terrestrial systems. These phenomena can cause geomagnetic storms that disrupt satellite communications, degrade GPS accuracy, overload power grids, and impair aviation and communication networks. Understanding the mechanisms and impacts of space weather is crucial for developing predictive capabilities to mitigate these risks. This study investigates the physical drivers of space weather and introduces a machine learning (ML)-driven framework for real-time monitoring and prediction. Leveraging multi-source data from satellites and ground-based observatories, the system incorporates advanced sunspot classification models based on the McIntosh and Wilson methods. By analyzing solar images and magnetograms with convolutional and recurrent neural networks, the framework achieves significant improvements in prediction accuracy for solar flares and geomagnetic disturbances. Furthermore, the integration of data-driven technologies with traditional observation methods provides timely and actionable forecasts, enhancing resilience against the cascading effects of space weather disruptions on global infrastructure. This research sets a foundation for scalable solutions to safeguard critical systems and supports proactive decision-making in space weather preparedness.