用深度学习自动识别航空安全报告中的飞行阶段，提升分析效率。
Aviation Safety Enhancement via NLP & Deep Learning: Classifying Flight Phases in ATSB Safety Reports
- 用LSTM等模型分析安全报告文本，识别飞行阶段
- LSTM模型准确率达88%，各项指标表现最优
- 适合航空安全研究者与智能报告处理开发者
航空安全至关重要，需对不同飞行阶段的安全事件进行精准分析。本研究采用自然语言处理（NLP）与深度学习模型（包括LSTM、CNN、双向LSTM和简单循环神经网络）对澳大利亚交通安全局（ATSB）安全报告中的飞行阶段进行分类。各模型均表现出高准确率、精确率、召回率和F1分数，其中LSTM表现最佳，各项指标分别为87%、88%、87%和88%。结果表明，该方法在自动化安全事件分析方面具有显著有效性。NLP与深度学习技术的融合有望显著提升航空安全分析能力，支持针对性安全措施制定并简化报告处理流程。
原文摘要 · Abstract (English)
Aviation safety is paramount, demanding precise analysis of safety occurrences during different flight phases. This study employs Natural Language Processing (NLP) and Deep Learning models, including LSTM, CNN, Bidirectional LSTM (BLSTM), and simple Recurrent Neural Networks (sRNN), to classify flight phases in safety reports from the Australian Transport Safety Bureau (ATSB). The models exhibited high accuracy, precision, recall, and F1 scores, with LSTM achieving the highest performance of 87%, 88%, 87%, and 88%, respectively. This performance highlights their effectiveness in automating safety occurrence analysis. The integration of NLP and Deep Learning technologies promises transformative enhancements in aviation safety analysis, enabling targeted safety measures and streamlined report handling.
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