ANALYSIS OF THE PRIMARY FACTORS AFFECTING THE MOST FATAL AVIATION ACCIDENTS: A MACHINE LEARNING APPROACH


İnan T. T., İnan N. G.

Reliability: Theory and Applications, vol.17, no.1, pp.164-177, 2022 (Scopus) identifier

  • Publication Type: Article / Article
  • Volume: 17 Issue: 1
  • Publication Date: 2022
  • Doi Number: 10.24412/1932-2321-2022-167-164-177
  • Journal Name: Reliability: Theory and Applications
  • Journal Indexes: Scopus
  • Page Numbers: pp.164-177
  • Keywords: classification of survivor/non-survivor passengers, fatal aviation accidents, machine learning, multivariate statistical analysis, primary causes
  • Istanbul Technical University Affiliated: Yes

Abstract

© 2022 Gnedenko Forum. All Rights Reserved.The safety concept is primarily examined in this study considering the most fatal accidents in aviation history with human, technical, and sabotage/terrorism factors. Although the aviation industry was started with the first engine flight in 1903, the safety concept has been examined since the beginning of the 1950s. However, the safety concept was firstly examined with technical factors, in the late 1970s, human factors have started to analyze. Despite these primary causes, there have other factors which could have an impact on accidents. So, the purpose of the study is to determine the affecting factors of the most fatal 100 accidents including aircraft type, distance, flight phase, primary cause, number of total passengers, and time period by classifying survivor/non-survivor passengers. Logistic regression and discriminant analysis are used as multivariate statistical analyses to compare with the machine learning approaches in terms of showing the algorithms’ robustness. Machine learning techniques have better performance than multivariate statistical methods in terms of accuracy (0.910), false-positive rate (0.084), and false-negative rate (0.118). In conclusion, flight phase, primary cause, and total passenger numbers are found as the most important factors according to machine learning and multivariate statistical models for classifying the accidents’ survivor/non-survivor passengers.