An Innovative Approach to Modeling Aviation Safety Incidents

Shi, Donghui
Cao, Shuai
Zurada, Jozef
Guan, Jian
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Due to the complexity of aviation safety operations, the number of flight incidents continues to rise. The Aviation Safety Reporting System (ASRS) contains the largest collection of such incidents. Efficient and effective analysis of these incidents remains a challenge. This paper proposes a new approach to analyze aviation safety records using deep learning methods to improve incident classification. The proposed approach, CNN-LSTM, combines the characteristics of convolutional neural network (CNN) and long short-term memory (LSTM) neural network, and a distributed computing method to model aviation safety data. The five machine learning methods Logistic Regression, Naive Bayes, Random Forest, Support Vector Machine, Multi-layer Perceptron were used to compare with CNN-LSTM. The results show that CNN-LSTM model can significantly improve the accuracy rates of classification for aviation safety incident reports using Word2Vec. The distributed platform in Spark with clusters can make full use of computing resources when processing textual data from ASRS, reducing time-consumption greatly when compared with machine learning algorithms running on a standalone computer. Timely and accurate identification of causes of reported incidents is important. The results of this study demonstrate a new approach to improve both accuracy and efficiency in incident cause identification.
Case studies of Artificial Intelligence, Business Intelligence, Analytics Technologies for Industry Platforms, aviation safety reporting system (asrs), human factors vs nonhuman factors, incidents cause classification, spark distributed platform
10 pages
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Proceedings of the 55th Hawaii International Conference on System Sciences
Attribution-NonCommercial-NoDerivatives 4.0 International
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