DocumentCode
605922
Title
Data mining approaches for aircraft accidents prediction: An empirical study on Turkey airline
Author
Christopher, A.B.A. ; Appavu, Subramanian
Author_Institution
Anna Univ., Chennai, India
fYear
2013
fDate
25-26 March 2013
Firstpage
739
Lastpage
745
Abstract
Data mining approaches have been successfully applied in different fields. Risk and safety have always been important considerations in aviation. There is a large amount of knowledge and data accumulation in aviation industry. These data can be store in the form of pilot reports, maintenance reports, accident reports or delay reports. This paper applied the decision tree model on accident reports of the Federal Aviation Administration (FAA) Accident / incident Data System database, contains 468 accident data records for all categories of aviation between the years of 1970 to 2011. The decision tree classifier is use to predict the warning level of the component as the class attribute. We have explored the use of the decision tree technique on aviation components data. Decision Tree induction algorithm is applied to generate the model and the generated model is used to predict the warning of accidents in the airline database. This work may be useful for Aviation Company to make better prediction.
Keywords
aerospace safety; data mining; decision trees; travel industry; FAA; Turkey airline; accident incident data system database; accident reports; aircraft accidents prediction; aviation industry; data accumulation; data mining approaches; decision tree induction algorithm; decision tree technique; delay reports; federal aviation administration; maintenance reports; pilot reports; Accidents; Aircraft; Data mining; Data models; Databases; Decision trees; Safety; classifier; data mining; decision tree induction; risk; safety;
fLanguage
English
Publisher
ieee
Conference_Titel
Emerging Trends in Computing, Communication and Nanotechnology (ICE-CCN), 2013 International Conference on
Conference_Location
Tirunelveli
Print_ISBN
978-1-4673-5037-2
Type
conf
DOI
10.1109/ICE-CCN.2013.6528602
Filename
6528602
Link To Document