首页|Improving port state control through a transfer learning-enhanced XGBoost model

Improving port state control through a transfer learning-enhanced XGBoost model

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  • NETL
  • NSTL
  • Elsevier
© 2024 Elsevier LtdWith the advancements in modern information technology, Port State Control (PSC) inspections, as a crucial measure to protect ship safety and the marine environment, are undergoing an intelligent transformation. This paper aims to streamline the selection process for inspecting high-risk ships by employing a data-driven model to predict the number of deficiencies in ships arriving at ports. A transfer learning-enhanced eXtreme Gradient Boosting (XGBoost) model is proposed by innovatively incorporating sample similarity calculations to adapt the model to the unique characteristics of the target port. This novel model enables the integration of relevant data from other ports, enhancing the predictive performance of the model to specific port conditions. Utilizing PSC inspection records from ports within the Tokyo Memorandum of Understanding (MoU) and choosing the port of Singapore as the target, numerical experiments demonstrate that the proposed model achieves improvements of 1.81 %, 6.08 %, and 3.60 % in the mean absolute error, mean squared error and root mean squared error, respectively, compared to the standard XGBoost model. Furthermore, across various sizes of training samples, the proposed model outperforms other machine learning models. This work may service as a significant step towards exploring the potential of developing data-driven models based on comprehensive data to assess the risk level of foreign ships arriving at ports, ameliorating the PSC inspection process by aiding PSC officers in identifying substandard ships more effectively.

Deficiency predictionPort state control inspectionTransfer learningXGBoost

Wang R.、Yan R.、Zhang M.、Gong F.、Wang S.

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School of Civil and Environmental Engineering Nanyang Technological University

Department of Mechanical Engineering School of Engineering Aalto University

Shenzhen Maritime Safety Administration of P.R.C

Department of Innovation Center COSCO Shipping Technology Co. Ltd.

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2025

10.1016/j.ress.2024.110558

Reliability engineering & system safety

Reliability engineering & system safety

SCI
ISSN：0951-8320
年,卷(期)：2025.253(Jan.)
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