Abstract: In order to grasp causes of bounced landing of aircraft which is a frequently occurring issue during flight operation, and effectively prevent such incidents, a prediction method for bounced landing based on DBN was proposed. Secondly, correlation between incidents and landing airports' environment was evaluated by using aviation data, and with an actual incident of China Eastern Airlines as an example, changing trend of bounced landing along with throttle stick position at touchdown was explored. Then, impacts of pilot control, aircraft status and unstable approach on incidents were discussed. Finally, different combinations of information were used as inputs to train the model, and their prediction accuracy was compared to find optimal one. The results show that DBN-based method is suitable for predicting bounced landing by utilizing flight data. When network input includes direct influencing factors such as airports' environment, throttle lever position, as well as indirect ones like unstable approach, this model can accurately predict accidents with a prediction accuracy as high as 94.78%.

Key words: deep belief network (DBN), bounced landing, quick access recorder (QAR), big data of aviation, unsafe incident