Machine learning approaches for identifying substandard ships in port state control inspections with imbalanced data

Wang, L, Wang, J, Hua, Y, Shi, W, Yang, Z ORCID: 0000-0003-1385-493X and Sha, M (2026) Machine learning approaches for identifying substandard ships in port state control inspections with imbalanced data. Ocean Engineering, 334. ISSN 0029-8018

Preview

Text Machine learning approaches for identifying substandard ships in port state control inspections with imbalanced data.pdf - Published Version Available under License Creative Commons Attribution. Download (5MB) | Preview

Abstract

To effectively identify substandard ships in port state control (PSC) inspections, this study proposes a novel machine learning (ML) approach that integrates support vector machines (SVMs) and deep belief networks (DBNs). A comprehensive dataset sourced from the Paris Memorandum of Understanding (MoU) inspection database is used, covering the years 2018–2020. The proposed ML approach effectively addresses the imbalanced data problem in PSC inspections, where the probability of substandard ships is typically no more than 5 %. The results of prediction accuracy indicate that the ensembled SVM-DBN model outperforms the standalone SVM model on both the original and oversampling datasets in all scenarios, except for a slightly lower precision rate on the oversampling dataset. In contrast, the SVM-DBN model underperforms relative to the standalone SVM model on the under-sampling dataset. These results have practical implications for both shipowners and port authorities. Shipowners can use the results to proactively assess and improve ship conditions, thereby mitigating potential detention risks, and port authorities can use them to improve risk-based inspection strategies, thereby optimizing inspection resources and increasing the effectiveness of substandard ship identification.

Item Type:	Article
Uncontrolled Keywords:	Port state control; Substandard ship identification; Imbalanced data; Machine learning; SVM-DBN model; 4012 Fluid Mechanics and Thermal Engineering; 4005 Civil Engineering; 4015 Maritime Engineering; 40 Engineering; Machine Learning and Artificial Intelligence; Networking and Information Technology R&D (NITRD); 0405 Oceanography; 0905 Civil Engineering; 0911 Maritime Engineering; Civil Engineering; 4005 Civil engineering; 4012 Fluid mechanics and thermal engineering; 4015 Maritime engineering
Subjects:	T Technology > T Technology (General) T Technology > T Technology (General) > T58.5 Information Technology
Divisions:	Engineering
Publisher:	Elsevier
Date of acceptance:	18 May 2025
Date of first compliant Open Access:	23 July 2026
Date Deposited:	23 Jul 2026 14:18
Last Modified:	23 Jul 2026 14:18
DOI or ID number:	10.1016/j.oceaneng.2025.121614
URI:	https://researchonline.ljmu.ac.uk/id/eprint/29056

View Item