Date of Award
3-26-2020
Document Type
Thesis
Degree Name
Master of Science in Operations Research
Department
Department of Operational Sciences
First Advisor
Raymond R. Hill, PhD
Abstract
The United States Air Force has a pilot shortage. Unfortunately, training an Air Force pilot requires significant time and resources. Thus, diligence and expediency are critical in selecting those pilot candidates with a strong possibility of success. This research applies multivariate and statistical machine learning techniques to pilot candidates pre-qualification test data and undergraduate pilot training results to determine whether there are selected pre-qualification tests or specific training evaluations that do a \best" job of screening for successful pilot training candidates and distinguished graduates. Flight experience, both during training and otherwise, indicates pilot training completion and performance.
AFIT Designator
AFIT-ENS-MS-20-M-150
DTIC Accession Number
AD1101488
Recommended Citation
Giddings, Aaron C., "Predicting Pilot Success Using Machine Learning" (2020). Theses and Dissertations. 3196.
https://scholar.afit.edu/etd/3196