A model fusion strategy for identifying aircraft risk using CNN and Att-BiLSTM

The Aviation Safety Reporting System (ASRS) data is an important information source for risk identification in civil aviation. However, ASRS data has the characteristics of high dimensionality, unstructured and data imbalance. This brings great challenges for risk identification. In this paper, a model fusion strategy is proposed for identifying the aircraft risk by using convolutional neural network (CNN) and bidirectional long short-term memory neural network with attention mechanism (Att-BiLSTM). Firstly, all obtained ASRS data are reasonably divided into five risk levels. Secondly, the CNN is utilized to deeply mine the relationship between risk levels and structured data. Meanwhile, the Att-BiLSTM is adopted to deeply mine the relationship between risk levels and unstructured event narratives. Finally, a model fusion strategy is proposed to fuse the preliminary identifications of CNN and Att-BiLSTM to obtain the final identifications. The adopted CNN and Att-BiLSTM are compared with two individual models respectively. The experimental results show that the CNN and Att-BiLSTM have superior identification performance. Furthermore, by comparing the performance of the proposed model fusion strategy against the effect of using the individual model, the proposed model fusion strategy is effective in improving identification accuracy and reducing the influence of data imbalance in civil aviation.