Using the Counterpropagation Fuzzy Neural Network to Predict the Flight Safety Performance of Taiwan Airlines

• Main Authors: Cheng-Wei Wang, 王政為
• Other Authors: Yu-Lin Hsiao
• Format: Others
• Language: zh-TW
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/54827543095245294152

碩士 === 中原大學 === 工業與系統工程研究所 === 104 === Accurate risk assessment of flight operation is always an essential topic to the aviation industry. Preventing accidents from happening is important to both the airliners and the public. Among the root causes of flight accidents, human factors is the most significant one. In this study, we utilized the quantitative data transformed via HFACS-MA form the historical safety inspection records of two domestic airlines from 2002-2008. The data was inputted to investigate the causal relationship between human factors and flight incident rates as the foundation of risk assessment methods. Counterpropagation Fuzzy Neural Network (CFNN) was adopted to develop the prediction model. The results found that CFNN method was better than Backpropagation (BP). The results of sensitivity analysis found that the incident rate of the current month and previous month were vital predictors for safety performance. Among the four kinds of human factors, unsafe supervision and precondition for unsafe acts had more impacts to the safety performance than unsafe acts and organizational influences. The contributions of this study are to support the causality of human factors and safety performance (incident rates), and to identify the impact of the human factors to flight safety. The CFNN method could be considered as one of the various quantitative tools while establishing risk assessment models for airlines under different context.