Abstract
Safety is the most significant factor that affected incidents (non-fatal) and accidents (fatal) in civil aviation history related to scheduled flights. In the history of scheduled flights, the total incident and accident number until 2022 is 1988. In this study, 677 of them are taken into consideration since 11 September 2001. The purpose of this study is to reveal the factors that can classify type of aircraft damages such as none, minor and substantial in all-time incidents and accidents. ML algorithms with different configurations are applied for the classification process. The RFE and PCA are used to find the most important factors that are effective on the classification. Four components are found with PCA as zone, weather, time, and history. The results of multinomial logistic regression and ANNs showed that the most important 5 features are latitude, wind speed, wind direction, year, and longitude to classify aircraft damage. Then, temperature, total number of injury passenger, and month factors comes with more than 50% importance. The managerial implication of the study shows that as time passes the number of substantial accidents has decreased due to increasing level of safety precautions in civil aviation.
Acknowledgements
Declarations
Author has no personal interests from other parties.
Ethical Approval
Ethical approval is not needed due to open source resources.
Availability of Data and Materials
The data was obtained from the websites that are mentioned below.
Aviation Safety Network. (2022). https://aviation-safety.net/database/record.php
Graham-Ely Aircraft Incidents. (2022). https://github.com/graham-ely/4460-p5/blob/master/aircraft_incidents.csv
Kathryns Report. (2022). https://www.kathrynsreport.com/2017/08/piper-pa-31-350-n4078j-marianas.html
Competing interests
The author declares no potential conflicts of interest about the publication of this article.
Authors' contributions
Data curation, Conceptualization, Investigation, Writing, Original draft preparation, Reviewing and Editing, Supervision, Resources, Methodology, Validation, Software, Formal analysis, Visualization.
Funding
Any funding received.
Acknowledgement
The author declares no potential conflicts of interest about the publication of this article.
The Financial Statement
In this manuscript, there has not any institution about taking financial aid.
Scholarly Commons Citation
İnan, T. T.
(2023).
Aircraft Damage Classification by using Machine Learning Methods.
International Journal of Aviation, Aeronautics, and Aerospace,
10(2).
DOI: https://doi.org/10.58940/2374-6793.1810
