Date of Award
3-2025
Document Type
Thesis
Degree Name
Master of Science
Department
Department of Operational Sciences
First Advisor
Nathan B. Gaw, PhD
Abstract
Solar Particle Events (SPEs) are high-energy phenomena from the Sun that pose risks to technology, human health, and Air Force operations. Accurate prediction of SPEs exceeding 100 MeV is crucial for mitigating these risks. This thesis explores using Bayesian statistical models to predict such events, integrating prior knowledge from solar physics with the ability to update predictions based on new data. The research uses a dataset spanning three solar cycles (21–23) and incorporates attributes like flare fluence, peak flux, latitude, longitude, and class. Four Bayesian models (PyMC, Bnlearn, and two Dredge models) were compared to machine learning models. The Bayesian models outperformed others, with the Bnlearn model achieving the highest accuracy, recall, and F1 scores. Key predictors included flare peak flux, fluence, and longitude, with longitude showing an inverse relationship.
AFIT Designator
AFIT-ENS-MS-25-M-173
Recommended Citation
Traub, Haley, "Machine Learning Techniques to Predict Solar Particle Events and Radiation of Aircrew" (2025). Theses and Dissertations. 8282.
https://scholar.afit.edu/etd/8282
Comments
An embargo was observed for this posting.
Distribution A: Approved for public release, Distribution Unlimited. PA case number 88ABW-2025-0255