An ensemble model using temporal convolution and dual attention gated recurrent unit to analyze risk of civil aircraft

Abstract

As the most important issue in civil aviation, safety needs to be maintained on an acceptable risk level in the air transport system at all times. Due to the high safety requirements in civil aviation, effective emergency response to high-risk events is of great significance for safety insurance. Therefore, it is very necessary to judge the risk level immediately after receiving the unsafe event reports. Unsafe event reports collected in the Aviation Safety Report System (ASRS) contain highly unstructured short texts, which pose a challenge for rapid risk analysis. In this paper, a risk analysis model that integrates a temporal convolutional network (TCN) and a dual attention gated recurrent unit (DAGRU) neural network is proposed for predicting the risk of civil aircraft based on the ASRS report. On the one hand, the causal convolution and dilated convolution structures of TCN are utilized to obtain higher-level text sequence features. On the other hand, the gated recurrent unit (GRU) neural network with dual attention gates is used to learn the contextual information of ASRS report. The introduced attention mechanism of GRU pays much attention to the significant information during the prediction procedure. The ability of GRU to solve long-term dependency problems is greatly enhanced. Finally, in order to verify the validity and reliability of the proposed ensemble model, the proposed TCN-DAGRU is compared with seven benchmark models. The comparison results show that the proposed TCN-DAGRU ensemble model can achieve the highest prediction accuracy and provide reliable support for rapid prediction of aviation safety risk.