A multi-source data-driven approach for navigation safety integrating computational intelligence and Bayesian networks
Ships often face various risks when sailing at sea, ranging from harsh natural environments to complex traffic conditions. To reduce the impact of these risks on ships and crews, this paper proposes a navigation risk assessment method that integrates computational intelligence (CI) techniques, such...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Marine Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmars.2025.1547305/full |
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| author | Xiaotong Qu Chengbo Wang Chengbo Wang |
| author_facet | Xiaotong Qu Chengbo Wang Chengbo Wang |
| author_sort | Xiaotong Qu |
| collection | DOAJ |
| description | Ships often face various risks when sailing at sea, ranging from harsh natural environments to complex traffic conditions. To reduce the impact of these risks on ships and crews, this paper proposes a navigation risk assessment method that integrates computational intelligence (CI) techniques, such as fuzzy logic, with Bayesian networks (BNs) and utility theory. Firstly, a navigation risk assessment system is established using maritime data and expert knowledge, which evaluates risks from a spatial perspective by considering factors such as safeguard and accident conditions across different regions. Secondly, a fuzzy logic-based numerical and expert data transformation method is proposed to derive the prior probabilities of risk factors in BNs. The weighted fuzzy rule base is used to capture the dependencies among the risk factors. Finally, the probability distribution of navigation risk is determined by combining the prior probability and the dependencies, which are converted into risk index values through utility theory. Taking the grid-based navigation risk assessment of the South China Sea as an example, the effectiveness of this method is verified. The results of the study provide theoretical support for navigation risk assessment based on multi-source data and provide a reference for formulate maritime regulatory policies. |
| format | Article |
| id | doaj-art-5796d4e9d1e2440990b3b6aaeeff3c21 |
| institution | Kabale University |
| issn | 2296-7745 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Marine Science |
| spelling | doaj-art-5796d4e9d1e2440990b3b6aaeeff3c212025-02-03T05:11:56ZengFrontiers Media S.A.Frontiers in Marine Science2296-77452025-02-011210.3389/fmars.2025.15473051547305A multi-source data-driven approach for navigation safety integrating computational intelligence and Bayesian networksXiaotong Qu0Chengbo Wang1Chengbo Wang2Integrated Transport Institute, Transportation Engineering College, Dalian Maritime University, Dalian, ChinaDepartment of Automation, School of Information Science and Technology, University of Science and Technology of China, Hefei, ChinaHubei Key Laboratory of Inland Shipping Technology, Wuhan, ChinaShips often face various risks when sailing at sea, ranging from harsh natural environments to complex traffic conditions. To reduce the impact of these risks on ships and crews, this paper proposes a navigation risk assessment method that integrates computational intelligence (CI) techniques, such as fuzzy logic, with Bayesian networks (BNs) and utility theory. Firstly, a navigation risk assessment system is established using maritime data and expert knowledge, which evaluates risks from a spatial perspective by considering factors such as safeguard and accident conditions across different regions. Secondly, a fuzzy logic-based numerical and expert data transformation method is proposed to derive the prior probabilities of risk factors in BNs. The weighted fuzzy rule base is used to capture the dependencies among the risk factors. Finally, the probability distribution of navigation risk is determined by combining the prior probability and the dependencies, which are converted into risk index values through utility theory. Taking the grid-based navigation risk assessment of the South China Sea as an example, the effectiveness of this method is verified. The results of the study provide theoretical support for navigation risk assessment based on multi-source data and provide a reference for formulate maritime regulatory policies.https://www.frontiersin.org/articles/10.3389/fmars.2025.1547305/fullnavigation safetyrisk assessmentcomputational intelligencemulti-source dataBayesian network |
| spellingShingle | Xiaotong Qu Chengbo Wang Chengbo Wang A multi-source data-driven approach for navigation safety integrating computational intelligence and Bayesian networks Frontiers in Marine Science navigation safety risk assessment computational intelligence multi-source data Bayesian network |
| title | A multi-source data-driven approach for navigation safety integrating computational intelligence and Bayesian networks |
| title_full | A multi-source data-driven approach for navigation safety integrating computational intelligence and Bayesian networks |
| title_fullStr | A multi-source data-driven approach for navigation safety integrating computational intelligence and Bayesian networks |
| title_full_unstemmed | A multi-source data-driven approach for navigation safety integrating computational intelligence and Bayesian networks |
| title_short | A multi-source data-driven approach for navigation safety integrating computational intelligence and Bayesian networks |
| title_sort | multi source data driven approach for navigation safety integrating computational intelligence and bayesian networks |
| topic | navigation safety risk assessment computational intelligence multi-source data Bayesian network |
| url | https://www.frontiersin.org/articles/10.3389/fmars.2025.1547305/full |
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