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

TL;DR

This study demonstrates that NLP and deep learning models, specifically sRNN and ResNet, can classify flight phases from unstructured safety reports with high accuracy, aiding aviation safety analysis.

Contribution

The paper introduces the application of NLP and deep learning models to classify flight phases from safety narratives, showing improved accuracy over simpler models.

Findings

Both models achieved over 68% accuracy in classification.

sRNN significantly outperformed the ResNet model.

Models demonstrated high precision, recall, and F1 scores.

Abstract

The air transport system recognizes the criticality of safety, as even minor anomalies can have severe consequences. Reporting accidents and incidents play a vital role in identifying their causes and proposing safety recommendations. However, the narratives describing pre-accident events are presented in unstructured text that is not easily understood by computer systems. Classifying and categorizing safety occurrences based on these narratives can support informed decision-making by aviation industry stakeholders. In this study, researchers applied natural language processing (NLP) and artificial intelligence (AI) models to process text narratives to classify the flight phases of safety occurrences. The classification performance of two deep learning models, ResNet and sRNN was evaluated, using an initial dataset of 27,000 safety occurrence reports from the NTSB. The results demonstrated good performance, with both models achieving an accuracy exceeding 68%, well above the random guess rate of 14% for a seven-class classification problem. The models also exhibited high precision, recall, and F1 scores. The sRNN model greatly outperformed the simplified ResNet model architecture used in this study. These findings indicate that NLP and deep learning models can infer the flight phase from raw text narratives, enabling effective analysis of safety occurrences.