Keywords

Aircraft accidents, Data mining, Dimension reduction (Statistics), Errors

Abstract

A study investigating what factors are present leading to pilots submitting voluntary anomaly reports regarding their flight performance was conducted. The study employed statistical methods, text mining, clustering, and dimensional reduction techniques in an effort to determine relationships between factors and anomalies. A review of the literature was conducted to determine what factors are contributing to these anomalous incidents, as well as what research exists on human error, its causes, and its management. Data from the NASA Aviation Safety Reporting System (ASRS) was analyzed using traditional statistical methods such as frequencies and multinomial logistic regression. Recently formalized approaches in text mining such as Knowledge Based Discovery (KBD) and Literature Based Discovery (LBD) were employed to create associations between factors and anomalies. These methods were also used to generate predictive models. Finally, advances in dimensional reduction techniques identified concepts or keywords within records, thus creating a framework for an unsupervised document classification system. Findings from this study reinforced established views on contributing factors to civil aviation anomalies. New associations between previously unrelated factors and conditions were also found. Dimensionality reduction also demonstrated the possibility of identifying salient factors from unstructured text records, and was able to classify these records using these identified features.

Notes

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Graduation Date

2010

Semester

Fall

Advisor

Karwowski, Waldemar

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Industrial Engineering and Management Systems

Format

application/pdf

Identifier

CFE0003463

URL

http://purl.fcla.edu/fcla/etd/CFE0003463

Language

English

Release Date

December 2010

Length of Campus-only Access

None

Access Status

Doctoral Dissertation (Open Access)

Subjects

Dissertations, Academic -- Engineering and Computer Science, Engineering and Computer Science -- Dissertations, Academic

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Engineering Commons

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