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Open AccessArticle

An Interpretable Aid Decision-Making Model for Flag State Control Ship Detention Based on SMOTE and XGBoost

by 1,2, 1,2,* and 1,2
1
School of Navigation, Wuhan University of Technology, Wuhan 430063, China
2
Hubei Key Laboratory of Inland Shipping Technology, Wuhan University of Technology, Wuhan 430063, China
*
Author to whom correspondence should be addressed.
Academic Editor: Jakub Montewka
J. Mar. Sci. Eng. 2021, 9(2), 156; https://doi.org/10.3390/jmse9020156
Received: 2 January 2021 / Revised: 2 February 2021 / Accepted: 2 February 2021 / Published: 4 February 2021
(This article belongs to the Special Issue Maritime Engineering, Industry Development Prospects)
The reasonable decision of ship detention plays a vital role in flag state control (FSC). Machine learning algorithms can be applied as aid tools for identifying ship detention. In this study, we propose a novel interpretable ship detention decision-making model based on machine learning, termed SMOTE-XGBoost-Ship detention model (SMO-XGB-SD), using the extreme gradient boosting (XGBoost) algorithm and the synthetic minority oversampling technique (SMOTE) algorithm to identify whether a ship should be detained. Our verification results show that the SMO-XGB-SD algorithm outperforms random forest (RF), support vector machine (SVM), and logistic regression (LR) algorithm. In addition, the new algorithm also provides a reasonable interpretation of model performance and highlights the most important features for identifying ship detention using the Shapley additive explanations (SHAP) algorithm. The SMO-XGB-SD model provides an effective basis for aiding decisions on ship detention by inland flag state control officers (FSCOs) and the ship safety management of ship operating companies, as well as training services for new FSCOs in maritime organizations. View Full-Text
Keywords: flag port control; ship detention decision; smart maritime; SMOTE algorithm; XGBoost flag port control; ship detention decision; smart maritime; SMOTE algorithm; XGBoost
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MDPI and ACS Style

He, J.; Hao, Y.; Wang, X. An Interpretable Aid Decision-Making Model for Flag State Control Ship Detention Based on SMOTE and XGBoost. J. Mar. Sci. Eng. 2021, 9, 156. https://doi.org/10.3390/jmse9020156

AMA Style

He J, Hao Y, Wang X. An Interpretable Aid Decision-Making Model for Flag State Control Ship Detention Based on SMOTE and XGBoost. Journal of Marine Science and Engineering. 2021; 9(2):156. https://doi.org/10.3390/jmse9020156

Chicago/Turabian Style

He, Jian; Hao, Yong; Wang, Xiaoqiong. 2021. "An Interpretable Aid Decision-Making Model for Flag State Control Ship Detention Based on SMOTE and XGBoost" J. Mar. Sci. Eng. 9, no. 2: 156. https://doi.org/10.3390/jmse9020156

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