You are here
A STUDY OF FACTORS CONTRIBUTING TO SELF-REPORTED ANOMALIES IN CIVIL AVIATION
- Date Issued:
- 2010
- Abstract/Description:
- 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.
Title: | A STUDY OF FACTORS CONTRIBUTING TO SELF-REPORTED ANOMALIES IN CIVIL AVIATION. |
45 views
25 downloads |
---|---|---|
Name(s): |
Andrzejczak, Chris, Author Karwowski, Waldemar, Committee Chair University of Central Florida, Degree Grantor |
|
Type of Resource: | text | |
Date Issued: | 2010 | |
Publisher: | University of Central Florida | |
Language(s): | English | |
Abstract/Description: | 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. | |
Identifier: | CFE0003463 (IID), ucf:48382 (fedora) | |
Note(s): |
2010-12-01 Ph.D. Engineering and Computer Science, Department of Industrial Engineering and Management Systems Masters This record was generated from author submitted information. |
|
Subject(s): |
human error dimensionality reduction text mining aviation incidents human factors |
|
Persistent Link to This Record: | http://purl.flvc.org/ucf/fd/CFE0003463 | |
Restrictions on Access: | public | |
Host Institution: | UCF |