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Safety Risk Analysis of Unmanned Ships in Inland Rivers Based on a Fuzzy Bayesian Network

Authors :
Zhang, Xiuxia
Zhang, Qingnian
Yang, Jie
Cong, Zhe
Luo, Jing
Chen, Huanwan
Source :
Journal of Advanced Transportation. December 31, 2019, Vol. 2019
Publication Year :
2019

Abstract

Risk factor identification is the basis for risk assessment. To quantify the safety risks of unmanned vessels in inland rivers, through analysis of previous studies, the safety risk impact factor framework of unmanned vessels in inland rivers is established based on three aspects: the ship aspect, the environmental aspect, and the management and control aspect. Relying on Yangtze River, a fuzzy Bayesian network of the sailing safety risk of unmanned ships in inland rivers is constructed. The proposed safety risk model has considered different operational and environmental factors that affect shipping operations. Based on the fuzzy set theory, historical data, and expert judgments and on previous works are used to estimate the base value (prior values) of various risk factors. The case study assessed the safety risk probabilities of unmanned vessels in Yangtze River. By running uncertainty and sensitivity analyses of the model, a significant change in the likelihood of the occurrence of safety risk is identified, and suggests a dominant factor in risk causation. The research results can provide effective information for analyzing the current safety status for navigation systems of unmanned ships in inland rivers. The estimated safety risk provides early warning to take appropriate preventive and mitigative measures to enhance the overall safety of shipping operations.<br />1. Introduction With the rapid development of science and technology, artificial intelligence has penetrated all aspects of human life more and more deeply. As an important branch of its development, [...]

Details

Language :
English
ISSN :
01976729
Volume :
2019
Database :
Gale General OneFile
Journal :
Journal of Advanced Transportation
Publication Type :
Academic Journal
Accession number :
edsgcl.613716187
Full Text :
https://doi.org/10.1155/2019/4057195