AI 中文总结
本研究将ML、NLP等高级AI方法应用于航空安全数据，挖掘事故模式、分析非结构化报告，为航空利益相关者提供数据驱动的安全决策支持。
AI 中文摘要
以严苛安全标准为特征的航空业对创新方法以强化安全措施的需求日益增长。尽管航空安全数据随时间大量积累，但其在预测和预防事故方面的全部潜力尚未被充分挖掘。本研究通过应用机器学习（ML）和自然语言处理（NLP）技术分析来自Socrata、澳大利亚运输安全局（ATSB）、美国国家运输安全委员会（NTSB）以及航空安全网（ASN）的航空安全数据，以填补这一空白。研究利用现有机器学习模型，包括深度学习、基于Transformer的架构，以及用于挖掘航空事故叙述的NLP方法，揭示导致事故和征候事故等安全相关事件的模式；此外，采用多种主题建模技术从非结构化安全报告中提取有意义的主题，提升事故分析的可解释性。本研究还进一步探索因果推断技术和可解释AI框架，以提高模型的透明度和可信度。本研究的一项关键贡献是在结构化航空安全场景中部署高级机器学习方法，评估其有效性并为实际应用提供见解。研究结果通过提供数据驱动的解决方案，为航空业利益相关者（包括监管机构、航空公司和政策制定者）提供关于事故分析和决策的宝贵见解，最终支持该行业持续努力降低风险、提升乘客与机组人员安全，并将AI驱动的方法整合到航空安全管理中。
英文摘要
The aviation industry characterized by its stringent safety standards has seen a growing need for innovative approaches to enhance safety measures. Despite the vast accumulation of aviation safety data over time, its full potential in predicting and preventing incidents has not been fully realized. This research addresses this gap by applying machine learning (ML) and natural language processing (NLP) techniques to analyze aviation safety data from Socrata, the Australian Transport Safety Bureau (ATSB), the National Transportation Safety Board (NTSB), and the Aviation Safety Network (ASN). By leveraging existing ML models, including deep learning and transformer-based architectures alongside NLP methods for mining aviation incident narratives, this study uncovers patterns contributing to safety related incidents such as accidents and near-misses. Additionally, it employs various topic modelling techniques to extract meaningful themes from unstructured safety reports, enhancing the interpretability of incident analysis. Causal inference techniques and interpretable AI frameworks are further explored to improve model transparency and trustworthiness. A key contribution of this work is the deployment of advanced ML methodologies in a structured aviation safety context, assessing their effectiveness and providing insights into their practical implementation. The findings offer valuable insights for aviation stakeholders, including regulators, airlines, and policymakers, by providing data-driven solutions that enhance incident analysis and decision making. Ultimately, this research supports the industry s ongoing efforts to minimize risks, improve passenger and crew security, and integrate AI driven methodologies into aviation safety management.
