Natural Language Processing and Deep Learning Models to Classify Phase of Flight in Aviation Safety Occurrences

Abstract

This study focuses on the classification of safety occurrences in the air transport system using natural language processing (NLP) and artificial intelligence (AI) models. The researchers utilized ResNet and sRNN deep learning models to classify flight phases based on unstructured text narratives of safety occurrence reports from the NTSB. The study evaluated the performance of these models using a dataset of 27,000 safety occurrence reports and found that both models achieved an accuracy exceeding 68%, surpassing the random guess rate of 14% for the seven-class classification problem. Additionally, the models exhibited high precision, recall, and F1 scores. Notably, the sRNN model outperformed the simplified ResNet model architecture used in the study. These findings suggest that NLP and deep learning models can effectively extract flight phase information from raw text narratives, enabling the thorough analysis of safety occurrences in the aviation industry.