Risk Index Prediction of Civil Aviation Based on Deep Neural Network

作     者：NI Xiaomei WANG Huawei CHE Changchang 

作者机构：College of Civil AviationNanjing University of Aeronautics and Astronautics 

出 版 物：《Transactions of Nanjing University of Aeronautics and Astronautics》 (南京航空航天大学学报（英文版）)

年 卷 期：2019年第36卷第2期

页      面：313-319页

核心收录：

学科分类：08[工学] 0802[工学-机械工程] 0825[工学-航空宇航科学与技术] 0704[理学-天文学] 

基　　金：supported by the Joint Funds of the National Natural Science Foundation of China (No. U1833110) 

主　　题：unsafe events risk index neural network denoising auto encoder 

摘      要：Safety is the foundation of sustainable development in civil aviation.Although catastrophic accidents are rare,indicators of potential incidents and unsafe events frequently materialize.Therefore,a history of unsafe data are considered in predicting safety risks.A deep learning method is adopted for extracting reactions in safety risks.The deep neural network(DNN)model for safety risk prediction is shown to extract complex data characteristics better than a shallow network model.Using extended unsafe data and monthly risk indices,hidden layers and iterations are determined.The effectiveness of DNN is also revealed in comparison with the traditional neural network.Through early risk detection using the method in the paper,airlines and the government can mitigate potential risk and take proactive measures to improve civil aviation safety.