Date of Graduation

5-2025

Document Type

Thesis

Degree Name

Bachelor of Science in Computer Science

Degree Level

Undergraduate

Department

Computer Science and Computer Engineering

Advisor/Mentor

Patitz, Matthew

Committee Member

Majumdar, Neelakshi

Second Committee Member

Gauch, John

Abstract

The NASA Aviation Safety Reporting System (ASRS) assembles voluntarily submitted aviation safety incident reports in their database to act on the information provided. This database allows the government, companies, and citizens to submit incident or situational reports to its database to discern recurring issues in the National Aviation System (NAS) so that the proper officials can act [1]. The narratives provided in these reports are text-based, resulting in large amounts of data to process. Previous work in the University of Arkansas Aerospace Systems Engineering and Transportation Laboratory (ASYST) lab involved parsing unmanned aircraft system (UAS) incident reports manually. While these reports can successfully be parsed by humans, this analysis is time consuming, technically challenging, and can lead to subjectivity. There are also many different factors that can play into each report, including what type of aircraft, location, or human factors. In my research, I analyze UAS incident narratives from January 2013–August 2023, involving operations under Federal Aviation Regulations (FAR) “Public Aircraft Operations (UAS) or Recreational Operations / Section 44809 (UAS)” using unsupervised machine learning to discern patterns and prevention of these incidents. Analyzing these using unsupervised machine learning methods offers a quicker way to discern patterns between the reports, allowing for analysis of UAS safety and incident prevention. My research will also contribute to a continuation of Dr. Majumdar’s research, by further expanding on her work with language processing to analyze these reports by using unsupervised machine learning methods. The findings from my research may assist UAS operators to be aware of causes and reduce incidents.

Keywords

machine learning; natural language processing; aviation safety; unmanned aircraft systems; unsupervised machine learning

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