Using Artificial Neural Network Methods to Predict the Safety Performance of the Domestic Airlines in Taiwan

• Main Authors: Yan-Ting Lin, 林彥廷
• Other Authors: Yu-Lin Hsiao
• Format: Others
• Language: zh-TW
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/13190502958963444893

碩士 === 中原大學 === 工業與系統工程研究所 === 103 === With the high-tech industry is constantly promoted, we concern at the safe management whether the capability is also more intact. The security is important and we should be aware of danger during work. We usually do a good safe supervision, when the incident will be happen, we can minimize the injury to loss. It is the most prior attention of the topic in the high-risk industry. In the air transportation, the safety should not be underestimated. When the accident will be occurred, it may involve a variety of conditions or factors. The major reason would be divided into human, machine and other of three factors. Among the human factor is a main cause in the aviation accident, as well as the human factor is not be easy to control at the all factors. How to establish an active preventive security management system enables the accident rate has dropped, it is an advance concept in the safe management. In this study, the audit data of two airlines were quantified. After transforming the audit data, it obtain per month of the human error rate. We utilize human error rate, in order to predict the incident rate in the future and try to establish an appropriate analysis model. We want to find out the potential factors of human error, which the reason will be caused the incident. The study adopted two neural network methods, Back-propagation (BP) and Radial basis function (RBF). We also compare the predictive performance under the different parameters in the two neural network methods and find the appropriate parameter configuration mode in the study. After using the configuration of different parameters, we found that not only the back-propagation (BP) has a good predictive performance than the radial basis function (RBF), but also search the best prediction mode of the back-propagation (BP). The result of the study is validated the causal relationship between the human factor and incident rate. By comparing the two methods, we consider that the back-propagation (BP) can produce appropriate flight safe prediction mode. It also can apply to construct the model with aviation safety quantitative management in the period of big data future. We hope that increase gradually predictive capability of the flight safe incident rate and thus improve flight accidents continue to occur. Finally, we also hope to assist airline industry to think about how to prevent accidents and enhance flight safety of our country.