Aviation accident and incident forecasting combining occurrence investigation and meteorological data using machine learning

Abstract

Studies on safety in aviation are necessary for the development of new technologies to forecast and prevent aeronautical accidents and incidents. When predicting these occurrences, the literature frequently considers the internal characteristics of aeronautical operations, such as aircraft telemetry and flight procedures, or external characteristics, such as meteorological conditions, with only few relationships being identified between the two. In this study, data from 6,188 aeronautical occurrences involving accidents, incidents, and serious incidents, in Brazil between January 2010 and October 2021, as well as meteorological data from two automatic weather stations, totaling more than 2.8 million observations, were investigated using machine learning tools. For data analysis, decision tree, extra trees, Gaussian naive Bayes, gradient boosting, and k-nearest neighbor classifiers with a high identification accuracy of 96.20% were used. Consequently, the developed algorithm can predict occurrences as functions of operational and meteorological patterns. Variables such as maximum take-off weight, aircraft registration and model, and wind direction are among the main forecasters of aeronautical accidents or incidents. This study provides insight into the development of new technologies and measures to prevent such occurrences.

Keywords

• air transport
• artificial intelligence
• aviation accident
• aviation incident
• innovation
• machine learning