A content-aware corpus-based model for analysis of marine accidents. (May 2023)

• Record Type: Journal Article
• Authors: Yan, Kai; Wang, Yanhui; Jia, Limin; Wang, Wenhao; Liu, Shengli; Geng, Yanbin
• Is Part Of: Accident analysis and prevention. Volume 184(2023)
• Publication Date: 2023-05
• Subjects: Marine accident -- Accident analysis -- Hazard identification -- Natural language processing -- Topic model; Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2023.106991
• Languages: English
• ISSNs: 0001-4575

Abstract

Highlights: A semi-automatic model is developed for hazard and accident cause identification. We explore and analyze the semantic relationship between the hazards of marine accidents. The importance of both hazards and accident causes is calculated and ranked. We gain accident causes distribution in 40 different accident scenarios by using topic model. Abstract: In the past decades, marine accidents brought the serious loss of life and property and environmental contamination. With the accumulation of marine accident data, especially accident investigation reports, compared with subjective reasoning based on expert experience, data-driven methods for analysis and accident prevention are more comprehensive and objective. This paper aims to develop a content-aware corpus-based model for the analysis of marine accidents to mine the accident semantic features. The general research framework is established to combine accident data, expert prior knowledge, and semi-automated natural language processing (NLP) technology. The NLP models are optimized, fused, and applied to the case study of ship collision accidents. The results show that the proposed model can accurately and quickly extract hazards, accident causes, and scenarios from the accident reports, and perform semantic analysis for the latent relationships between them to extend the accident causation theory. This study can provide a powerful and innovative analysis tool for marine accidents for maritime traffic safety management departments and relevant research institutions.