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

no code implementations • 27 Feb 2024 • Yasser Abduallah, Khalid A. Alobaid, Jason T. L. Wang, Haimin Wang, Vania K. Jordanova, Vasyl Yurchyshyn, Huseyin Cavus, Ju Jing

For example, SYMHnet achieves a forecast skill score (FSS) of 0. 343 compared to the FSS of 0. 074 of a recent gradient boosting machine (GBM) method when predicting SYM-H indices (1 hour in advance) in a large storm (SYM-H = -393 nT) using 5-minute resolution data.

Bayesian Inference Uncertainty Quantification

Predicting Solar Energetic Particles Using SDO/HMI Vector Magnetic Data Products and a Bidirectional LSTM Network

no code implementations • 27 Mar 2022 • Yasser Abduallah, Vania K. Jordanova, Hao liu, Qin Li, Jason T. L. Wang, Haimin Wang

Solar energetic particles (SEPs) are an essential source of space radiation, which are hazards for humans in space, spacecraft, and technology in general.