Symptom H Index

The SYM-H index quantifies geomagnetic activity, a key indicator of space weather disturbances impacting technological systems. Current research focuses on improving short-term SYM-H index forecasting using advanced machine learning techniques, such as recurrent neural networks (RNNs) and Bayesian deep learning models incorporating graph neural networks. These models leverage solar wind data to predict the onset and intensity of geomagnetic storms, aiming for more accurate and reliable predictions with quantified uncertainties. Improved forecasting capabilities are crucial for mitigating the risks posed by space weather to infrastructure and satellite operations.

Papers

March 14, 2024

Forecasting Geoffective Events from Solar Wind Data and Evaluating the Most Predictive Features through Machine Learning Approaches
Sabrina Guastavino, Katsiaryna Bahamazava, Emma Perracchione, Fabiana Camattari, Gianluca Audone, Daniele Telloni, Roberto Susino, Gianalfredo Nicolini, Silvano Fineschi, Michele Piana, Anna Maria Massone
Event Forecasting Geomagnetic Storm Solar Wind Magnetic Perturbation Symptom H Index

February 27, 2024

Prediction of the SYM-H Index Using a Bayesian Deep Learning Method with Uncertainty Quantification
Yasser Abduallah, Khalid A. Alobaid, Jason T. L. Wang, Haimin Wang, Vania K. Jordanova, Vasyl Yurchyshyn, Huseyin Cavus, Ju Jing
Human Prediction Uncertainty Quantification Bayesian Deep Learning Solar Wind Symptom H Index

Symptom H Index

Papers

Forecasting Geoffective Events from Solar Wind Data and Evaluating the Most Predictive Features through Machine Learning Approaches

Prediction of the SYM-H Index Using a Bayesian Deep Learning Method with Uncertainty Quantification