文献类型：期刊文献

英文题名：A data-driven ISM-BN model for safety analysis of inland shipping in the Pearl River Basin

作者：Li, Fang[1];Lin, Shengliang[1];Li, Heping[1];Yin, Jianchuan[2];Li, Dexin[1];Zhang, Jinshui[1]

机构：[1]Guangzhou Maritime Univ, Guangzhou 510700, Guangdong, Peoples R China;[2]Guangdong Ocean Univ, Zhanjiang 524088, Guangdong, Peoples R China

年份：2024

卷号：314

外文期刊名：OCEAN ENGINEERING

收录：SCI-EXPANDED(收录号：WOS:001355197000001)、、EI(收录号：20244517330825)、Scopus(收录号：2-s2.0-85208174493)、WOS

基金：The study received funding from multiple sources. The Hubei Provincial Department of Education's 2022 Scientific Research Program, China, project number B2022391, the Natural Science Foundation of Hubei Province, China, grant number 2022CFB285, the "2023 Guang-dong Provincial Ordinary Higher Education Young Innovative Talent Project, China", grand number 2023KQNCX061, "Guangzhou Municipal Education Bureau College Scientific Research Project, China" (No. 2024312003) and "Innovation and Entrepreneurship Training Program Project of University Student, China" contributed financial support. These funding sources facilitated research endeavors, enabling the study to advance its objectives and contribute to the academic field. We are also very grateful to the Pearl River Navigation Administration for providing us with data support.

语种：英文

外文关键词：Shipping safety; ISM; BN; Navigational risk factor; Inland waterway vessel

外文摘要：Inland shipping of the Pearl River plays an important role in the Chinese shipping system. To ensure navigation safety, we collect reports of maritime accidents from 2015 to 2022 in the Pearl River basin. This article extracts influencing factors by collecting the experience of inland waterway safety navigation and analyzing accident reports. Then, this paper uses the interpretative structural modeling method (ISM) to build a correlation model. Using a data-driven Bayesian network (BN), it analyzes the impact of various factors on the safety navigation in the Pearl River. The model validation is completed by compared with tree augmented naive Bayes classifiers (TAN) network using the same validation samples, through validation with the test set, the prediction accuracy has improved by 25%. The results indicate factors such as vessel type, accident month, accident day and time, etc. have a significant impact on the safety of navigation in the inland Pearl River waterway. The method used can identify important risk factors for accidents and the average predictive probability of validation samples reaches 87.03%. These research results could be extended to maritime management efforts in the Pearl River Basin.