Prediction of 10,000-hour rate of civil aviation incidents based on grey Markov model

Abstract

The 10,000-hour rate of civil aviation incidents is an important index parameter to measure flight safety. Predicting the development trend of the 10,000-hour rate of civil aviation incidents plays an important role in aviation accident prevention and safety decision-making. Many complex factors influence the occurrence of civil aviation incidents, so the 10,000-hour rate of civil aviation incidents changes randomly and volatilely. This study proposed the idea of prediction by combining the grey GM (1, 1) model and the Markov model. Specifically, the grey GM (1, 1) prediction model was constructed using the statistical data on the 10,000-hour rate of civil aviation incidents in China during 2005–2020. On this basis, a grey Markov prediction model was established. The prediction of the 10,000-hour rate of incidents in 2021 based on the two models showed that the grey Markov model displayed higher prediction accuracy than the grey GM (1, 1) model and conformed to the change laws of the 10,000-hour rate data of civil aviation incidents better. Moreover, the grey Markov model could effectively improve the accuracy of the grey prediction model, compensate for its deficiencies, and facilitate the mastery of the change laws of civil aviation incidents, providing a reliable basis for aviation safety management and incident prevention.