用AI从事故文本中自动识别飞行阶段，提升航空安全预警能力
Phase of Flight Classification in Aviation Safety using LSTM, GRU, and BiLSTM: A Case Study with ASN Dataset
- 用LSTM、GRU等RNN模型解析事故描述文本，识别飞行阶段
- BiLSTM达64%准确率，组合模型表现更优，最高67%
- 适合航空安全研究者与智能事故分析系统开发者
航空安全是行业核心关切，微小操作失误可能引发严重后果。本研究通过自然语言处理（NLP）与深度学习模型，从非结构化的航空事故分析文本中分类飞行阶段。目标是验证是否可从事故后的叙述中推断飞行阶段。评估了多种深度学习模型的分类性能：单个RNN模型中，LSTM准确率63%、精确率60%、召回率61%；BiLSTM准确率64%、精确率63%、召回率64%；GRU在准确率和召回率均为60%，精确率63%。联合模型进一步提升性能：GRU-LSTM、LSTM-BiLSTM、GRU-BiLSTM的准确率分别为62%、67%、60%。结果表明，这些模型能有效从原始文本中分类飞行阶段，为航空业决策提供支持，推动了NLP与深度学习在航空安全中的应用。
原文摘要 · Abstract (English)
Safety is the main concern in the aviation industry, where even minor operational issues can lead to serious consequences. This study addresses the need for comprehensive aviation accident analysis by leveraging natural language processing (NLP) and advanced AI models to classify the phase of flight from unstructured aviation accident analysis narratives. The research aims to determine whether the phase of flight can be inferred from narratives of post-accident events using NLP techniques. The classification performance of various deep learning models was evaluated. For single RNN-based models, LSTM achieved an accuracy of 63%, precision 60%, and recall 61%. BiLSTM recorded an accuracy of 64%, precision 63%, and a recall of 64%. GRU exhibited balanced performance with an accuracy and recall of 60% and a precision of 63%. Joint RNN-based models further enhanced predictive capabilities. GRU-LSTM, LSTM-BiLSTM, and GRU-BiLSTM demonstrated accuracy rates of 62%, 67%, and 60%, respectively, showcasing the benefits of combining these architectures. To provide a comprehensive overview of model performance, single and combined models were compared in terms of the various metrics. These results underscore the models' capacity to classify the phase of flight from raw text narratives, equipping aviation industry stakeholders with valuable insights for proactive decision-making. Therefore, this research signifies a substantial advancement in the application of NLP and deep learning models to enhance aviation safety.
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