Flight operation risk prediction model based on the multivariate chaotic time series

With the development of civil aviation safety management, the flight operation risk of airlines is of increasing concern. Risk prediction technology extracts information from historical and current risk data and uses it to predict short-term trends in the future, thus helping identify emerging risks and providing more time for risk management. Compared with non-dynamic risk assessment, this technology is more substantial for the management and control of flight operation risk. To improve the accuracy of flight operation risk prediction, on the basis of the flight risk data of a certain airline in 2016—2018, the chaotic characteristics of 15 risk time series were verified and a short-term risk prediction model based on the multivariate chaotic time series was constructed. First, multivariate phase space reconstruction was performed on 15 risk time series, and the phase space was reduced by the principal component analysis (PCA) method. Then, four short-term risk prediction models, namely, extreme learning machine, radial basis function (RBF) neural network, echo state network, and Elman neural network, were built on the basis of iterative prediction. Finally, the phase space after dimension reduction was used as the model input, and the risk prediction results for 1–7 d were calculated and compared. Results show that the total number of dimensions after multivariable phase space reconstruction is 62, which is reduced to 31 by PCA dimension reduction. Of the four prediction models, the RBF neural network model after dimension reduction has the best prediction effect. The occurrence frequency of <25% relative error is 82.62% for the first day and 75% for the fifth day. The corrected mean absolute percentage error for the first day is 11.32%, and lower than 20% for the next 4 d. Thus, the calculation results meet the requirements of the airline. The prediction results within 1–5 d have practical value for flight risk management, proving that the risk prediction method based on the multivariate chaotic time series is feasible and effective.