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.

翻译：航空运输系统认识到安全的重要性，即使微小异常也可能导致严重后果。事故与事件报告在识别其原因和提出安全建议方面发挥着至关重要的作用。然而，描述事故前事件的叙述以非结构化文本形式呈现，计算机系统难以直接理解。基于这些叙述对安全事件进行分类和归类，有助于航空业利益相关者做出明智决策。在本研究中，研究人员应用自然语言处理（NLP）与人工智能（AI）模型处理文本叙述，以对安全事件所属的飞行阶段进行分类。研究使用来自NTSB的27,000份安全事件报告作为初始数据集，评估了两种深度学习模型ResNet和sRNN的分类性能。结果表明，两种模型均表现出良好的性能，在七分类问题中准确率均超过68%，远高于随机猜测的14%。模型还展现出较高的精确率、召回率和F1分数。其中，sRNN模型的表现显著优于本研究中采用的简化ResNet模型架构。这些发现表明，NLP与深度学习模型能够从原始文本叙述中推断飞行阶段，从而实现对安全事件的有效分析。