A Comparative Overview of Accident Forecasting Approaches for Aviation Safety. Issue 1 (February 2021)

• Record Type: Journal Article
• Authors: Monika, ; Verma, Seema; Kumar, Pardeep
• Is Part Of: Journal of physics. Volume 1767:Issue 1(2021)
• Publication Date: 2021-02
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1767/1/012015
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: The demand of air transportation is expected to be doubled over the next two decades as per the recommendations of the International Air Transport Association. This would prompt more aviation safety issues with increased air traffic congestion and load on the air transportation system. To estimate the level of risk and improve the forecasting ability, various methodologies have been proposed by the research community. As each methodology has its pros and cons, this manuscript provides a comparative study of various Data mining, Time series, Artificial Neural Networks, and ensemble Techniques on the aviation safety and forecasting complication. This paper concludes that different methods dealing with different information may be combined to have an outstanding prospective in aviation accident forecasting and to come up with a number of ways of enhancement and their assistance in decision making.