Deep Learning Ensemble Model for Aviation Safety Risk Prediction  
Author Di Zhou


Co-Author(s) Xiao Zhuang; Hongfu Zuo; Xufeng Zhao; Jiawei Xiang


Abstract Safety is a constant topic in civil aviation. The Aviation Safety Reporting System (ASRS) database, which is established by the Federal Aviation Administration (FAA), is an important information source for predicting aircraft risks. ASRS is a data source composed of classification attributes and descriptive textual narratives of aviation accidents. It covers almost all operational scopes of aircraft that a mistake might be made. However, ASRS data has the characteristics of high dimensionality, unstructured and data imbalance. This brings great challenges to risk identify. In order to solve the challenge, an ensemble multi-deep learning neural network based on convolutional neural network (CNN) and bidirectional long short-term memory neural network with attention mechanism (Att-BiLSTM) is proposed for aviation incident risk prediction in this paper.


Keywords Aviation safety; Risk prediction; Convolutional neural networks; Long short-term memory
    Article #:  RQD27-44

Proceedings of 27th ISSAT International Conference on Reliability & Quality in Design
Virtual Event

August 4-6, 2022