Space Weather

Space weather encompasses the study of solar phenomena and their effects on Earth's environment, with the primary objective of improving prediction capabilities to mitigate risks to technological infrastructure and human activities. Current research heavily utilizes machine learning, employing diverse architectures like convolutional neural networks (CNNs), recurrent neural networks (RNNs, including LSTMs and biLSTMs), and hybrid classical-quantum models, often within ensemble frameworks, to forecast various space weather indices (e.g., Dst index, solar flare occurrence, proton flux) and events (e.g., CMEs, geomagnetic storms). These advancements are crucial for enhancing space mission operations, safeguarding power grids and satellite communications, and furthering our understanding of the Sun-Earth connection.