Advancing Maritime Safety: A Literature Review on Machine Learning and Multi-Criteria Analysis in PSC Inspections
This literature review provides a structured quantitative analysis of existing research on the application of machine learning models (MLMs) and multi-criteria decision-making methods (MCDM) in the context of port state control (PSC). The aim of the study is to capture current research trends, ident...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/5/974 |
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| _version_ | 1849711317398585344 |
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| author | Zlatko Boko Ivica Skoko Zaloa Sanchez Varela Vice Milin |
| author_facet | Zlatko Boko Ivica Skoko Zaloa Sanchez Varela Vice Milin |
| author_sort | Zlatko Boko |
| collection | DOAJ |
| description | This literature review provides a structured quantitative analysis of existing research on the application of machine learning models (MLMs) and multi-criteria decision-making methods (MCDM) in the context of port state control (PSC). The aim of the study is to capture current research trends, identify thematic priorities, and demonstrate how these analytical tools have been used to support decision-making and risk assessment in the maritime domain. Rather than evaluating the effectiveness of individual models, the study focuses on the distribution and frequency of their use and provides insights into the development of methodological approaches in this area. Although several studies suggest that the integration of MLMs and MCDM techniques can improve the objectivity and efficiency of PSC inspections, this report does not provide a comparative assessment of their performance. Instead, it lays the groundwork for future qualitative studies that will assess the practical benefits and challenges of such integration. The findings suggest a fragmented but growing research interest in data-driven approaches to PSC and highlight the potential of advanced analytics to support maritime safety and regulatory compliance. |
| format | Article |
| id | doaj-art-063a4f78dfdf49d6bddb7902d4c4ea61 |
| institution | DOAJ |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-063a4f78dfdf49d6bddb7902d4c4ea612025-08-20T03:14:39ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-05-0113597410.3390/jmse13050974Advancing Maritime Safety: A Literature Review on Machine Learning and Multi-Criteria Analysis in PSC InspectionsZlatko Boko0Ivica Skoko1Zaloa Sanchez Varela2Vice Milin3Faculty of Maritime Studies, University of Split, 21000 Split, CroatiaFaculty of Maritime Studies, University of Split, 21000 Split, CroatiaFaculty of Maritime Studies, University of Split, 21000 Split, CroatiaFaculty of Maritime Studies, University of Split, 21000 Split, CroatiaThis literature review provides a structured quantitative analysis of existing research on the application of machine learning models (MLMs) and multi-criteria decision-making methods (MCDM) in the context of port state control (PSC). The aim of the study is to capture current research trends, identify thematic priorities, and demonstrate how these analytical tools have been used to support decision-making and risk assessment in the maritime domain. Rather than evaluating the effectiveness of individual models, the study focuses on the distribution and frequency of their use and provides insights into the development of methodological approaches in this area. Although several studies suggest that the integration of MLMs and MCDM techniques can improve the objectivity and efficiency of PSC inspections, this report does not provide a comparative assessment of their performance. Instead, it lays the groundwork for future qualitative studies that will assess the practical benefits and challenges of such integration. The findings suggest a fragmented but growing research interest in data-driven approaches to PSC and highlight the potential of advanced analytics to support maritime safety and regulatory compliance.https://www.mdpi.com/2077-1312/13/5/974literature reviewmachine learning modelsmulti-criteria analysisport state controlmaritime safety |
| spellingShingle | Zlatko Boko Ivica Skoko Zaloa Sanchez Varela Vice Milin Advancing Maritime Safety: A Literature Review on Machine Learning and Multi-Criteria Analysis in PSC Inspections Journal of Marine Science and Engineering literature review machine learning models multi-criteria analysis port state control maritime safety |
| title | Advancing Maritime Safety: A Literature Review on Machine Learning and Multi-Criteria Analysis in PSC Inspections |
| title_full | Advancing Maritime Safety: A Literature Review on Machine Learning and Multi-Criteria Analysis in PSC Inspections |
| title_fullStr | Advancing Maritime Safety: A Literature Review on Machine Learning and Multi-Criteria Analysis in PSC Inspections |
| title_full_unstemmed | Advancing Maritime Safety: A Literature Review on Machine Learning and Multi-Criteria Analysis in PSC Inspections |
| title_short | Advancing Maritime Safety: A Literature Review on Machine Learning and Multi-Criteria Analysis in PSC Inspections |
| title_sort | advancing maritime safety a literature review on machine learning and multi criteria analysis in psc inspections |
| topic | literature review machine learning models multi-criteria analysis port state control maritime safety |
| url | https://www.mdpi.com/2077-1312/13/5/974 |
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