A New Method for Aviation Safety Prediction Based on the Highest Density Domain in Uncertainty Environment

Abstract

Based on the highest density domain analysis, a new algorithm is proposed to perform prediction of the aviation safety in an uncertain framework. In order to perform the prediction of the aviation safety, highest density domain (HDR) is combined with uncertainty description technique to obtain the aviation safety interval and the corresponding interval probability level in the proposed method. Firstly, the kernel density estimator is further utilized with to produce the probability distribution. Secondly, highest density regions based on the estimated probability density function have been considered, to further investigate the underlying information. Then the aviation safety interval and the corresponding interval probability level are given to complete the aviation safety prediction. Compared with the methods of traditional aviation safety causal prediction and aviation safety time series prediction, this paper considers the uncertainty properties of aviation safety more thoroughly. Discussions with the flight accident hourly rate of the US Air Force from 1991 to 1999 have evidenced the applicability and adaptability of the proposed method.