Ship detention prediction using anomaly detection in port state control: model and explanation

Ran Yan; Shuaian Wang; Ran Yan; Shuaian Wang

doi:10.3934/era.2022188

Electronic Research Archive

2022, Volume 30, Issue 10: 3679-3691. doi: 10.3934/era.2022188

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Research article Special Issues

Ship detention prediction using anomaly detection in port state control: model and explanation

Ran Yan ^,,
Shuaian Wang

Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong

Received: 24 June 2022 Revised: 21 July 2022 Accepted: 26 July 2022 Published: 03 August 2022

Maritime transport plays an important role in global supply chain. To guarantee maritime safety, protect the marine environment, and enhance the living and working conditions of the seafarers, international codes and conventions are developed and implemented. Port state control (PSC) is a critical maritime policy to ensure that ships comply with the related regulations by selecting and inspecting foreign visiting ships visiting a national port. As the major inspection result, ship detention, which is an intervention action taken by the port state, is dependent on both deficiency/deficiencies (i.e., noncompliance) detected and the judgement of the inspector. This study aims to predict ship detention based on the number of deficiencies identified under each deficiency code and explore how each of them influences the detention decision. We innovatively view ship detention as a type of anomaly, which refers to data points that are few and different from the majority, and develop an isolation forest (iForest) model, which is an unsupervised anomaly detection model, for detention prediction. Then, techniques in explainable artificial intelligence are used to present the contribution of each deficiency code on detention. Numerical experiments using inspection records at the Hong Kong port are conducted to validate model performance and generate policy insights.
- Port state control (PSC),
- ship detention,
- anomaly detection,
- isolation forest (iForest)
Citation: Ran Yan, Shuaian Wang. Ship detention prediction using anomaly detection in port state control: model and explanation[J]. Electronic Research Archive, 2022, 30(10): 3679-3691. doi: 10.3934/era.2022188

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Abstract

Maritime transport plays an important role in global supply chain. To guarantee maritime safety, protect the marine environment, and enhance the living and working conditions of the seafarers, international codes and conventions are developed and implemented. Port state control (PSC) is a critical maritime policy to ensure that ships comply with the related regulations by selecting and inspecting foreign visiting ships visiting a national port. As the major inspection result, ship detention, which is an intervention action taken by the port state, is dependent on both deficiency/deficiencies (i.e., noncompliance) detected and the judgement of the inspector. This study aims to predict ship detention based on the number of deficiencies identified under each deficiency code and explore how each of them influences the detention decision. We innovatively view ship detention as a type of anomaly, which refers to data points that are few and different from the majority, and develop an isolation forest (iForest) model, which is an unsupervised anomaly detection model, for detention prediction. Then, techniques in explainable artificial intelligence are used to present the contribution of each deficiency code on detention. Numerical experiments using inspection records at the Hong Kong port are conducted to validate model performance and generate policy insights.

References

[1]	M. Eiden, G. Abramowicz, W. Filipiak, D. Małyszko, J. Małyszko, K. Węcel, A framework for the quality-based selection and retrieval of open data-a use case from the maritime domain, Electron. Markets, 28 (2018), 219–233. https://doi.org/10.1007/s12525-017-0277-y doi: 10.1007/s12525-017-0277-y
[2]	S. Wang, X. Chen, X. Qu, Model on empirically calibrating stochastic traffic flow fundamental diagram, Commun. Transp. Res., 1 (2021), 100015. https://doi.org/10.1016/j.commtr.2021.100015 doi: 10.1016/j.commtr.2021.100015
[3]	R. Yan, S. Wang, L. Zhen, G. Laporte, Emerging approaches applied to maritime transport research: Past and future, Commun. Transp. Res., 1 (2021), 100011. https://doi.org/10.1016/j.commtr.2021.100011 doi: 10.1016/j.commtr.2021.100011
[4]	C. L. Tsai, D. T. Su, C. P. Wong, An empirical study of the performance of weather routing service in the North Pacific Ocean, Marit. Bus. Rev., 6 (2021), 280–292. https://doi.org/10.1108/MABR-11-2020-0066 doi: 10.1108/MABR-11-2020-0066
[5]	V. Zisi, H. N. Psaraftis, T. Zis, The impact of the 2020 global sulfur cap on maritime CO2 emissions, Marit. Bus. Rev., 6 (2021), 339–357. https://doi.org/10.1108/MABR-12-2020-0069 doi: 10.1108/MABR-12-2020-0069
[6]	L. Wu, Y. Adulyasak, J. F. Cordeau, S. Wang, Vessel service planning in seaports, Oper. Res., (2022) 2022. https://doi.org/10.1287/opre.2021.2228 doi: 10.1287/opre.2021.2228
[7]	UNCTAD, Review of maritime transport 2021, Accessed 20 December 2021. Available from: https://unctad.org/webflyer/review-maritime-transport-2021.
[8]	S. Wang, R. Yan, A global method from predictive to prescriptive analytics considering prediction error for "Predict, then optimize" with an example of low-carbon logistics, Cleaner Logist. Supply Chain, 4 (2022), 100062. https://doi.org/10.1016/j.clscn.2022.100062 doi: 10.1016/j.clscn.2022.100062
[9]	R. Yan, S. Wang, Integrating prediction with optimization: Models and applications in transportation management, Multimodal Transp., 1 (2022), 100018. https://doi.org/10.1016/j.multra.2022.100018 doi: 10.1016/j.multra.2022.100018
[10]	W. Yi, L. Zhen, Y. Jin, Stackelberg game analysis of government subsidy on sustainable off-site construction and low-carbon logistics, Cleaner Logist. Supply Chain, 2 (2021), 100013. https://doi.org/10.1016/j.clscn.2021.100013 doi: 10.1016/j.clscn.2021.100013
[11]	IMO, Maritime safety, Accessed 25 February 2022. Available from: https://www.imo.org/en/OurWork/Safety/Pages/default.aspx.
[12]	D. Huang, S. Wang, A two-stage stochastic programming model of coordinated electric bus charging scheduling for a hybrid charging scheme, Multimodal Transp., 1 (2022), 100006. https://doi.org/10.1016/j.multra.2022.100006 doi: 10.1016/j.multra.2022.100006
[13]	A. P. C. Chan, W. Yi, F. K. Wong, Evaluating the effectiveness and practicality of a cooling vest across four industries in Hong Kong, Facilities, 34 (2016), 511–534. https://doi.org/10.1108/F-12-2014-0104 doi: 10.1108/F-12-2014-0104
[14]	W. Yi, Y. Zhao, A. P. C. Chan, Evaluating the effectiveness of cooling vest in a hot and humid environment, Ann. Work Exposures Health, 61 (2017), 481–494. https://doi.org/10.1093/annweh/wxx007 doi: 10.1093/annweh/wxx007
[15]	W. Yi, S. Wu, L. Zhen, G. Chawynski, Bi-level programming subsidy design for promoting sustainable prefabricated product logistics, Cleaner Logist. Supply Chain, 1 (2021), 100005. https://doi.org/10.1016/j.clscn.2021.100005 doi: 10.1016/j.clscn.2021.100005
[16]	X. Chen, D. Z. Long, J. Qi, Preservation of supermodularity in parametric optimization: Necessary and sufficient conditions on constraint structures, Oper. Res., 69 (2021), 1–12. https://doi.org/10.1287/opre.2020.1992 doi: 10.1287/opre.2020.1992
[17]	J. Zhang, D. Z. Long, R. Wang, C. Xie, Impact of penalty cost on customers' booking decisions, Prod. Oper. Manage., 30 (2021), 1603–1614. https://doi.org/10.1111/poms.13297 doi: 10.1111/poms.13297
[18]	R. Yan, H. Mo, S. Wang, D. Yang, Analysis and prediction of ship energy efficiency based on the MRV system, Marit. Policy Manage., 2021 (2021), 1–23. https://doi.org/10.1080/03088839.2021.1968059 doi: 10.1080/03088839.2021.1968059
[19]	R. Yan, H. Mo, X. Guo, Y. Yang, S. Wang, Is port state control influenced by the COVID-19? Evidence from inspection data, Transp. Policy, 123 (2022), 82–103. https://doi.org/10.1016/j.tranpol.2022.04.002 doi: 10.1016/j.tranpol.2022.04.002
[20]	Z. Sun, R. Zhang, Y. Gao, Z. Tian, Y. Zuo, Hub ports in economic shocks of the melting Arctic, Marit. Policy Manage., 48 (2021), 917–940. https://doi.org/10.1080/03088839.2020.1752948 doi: 10.1080/03088839.2020.1752948
[21]	W. Ma, T. Lu, D. Ma, D. Wang, F. Qu, Ship route and speed multi-objective optimization considering weather conditions and emission control area regulations, Marit. Policy Manage., 48 (2021), 1053–1068. https://doi.org/10.1080/03088839.2020.1825853 doi: 10.1080/03088839.2020.1825853
[22]	IMO, Procedure for port state control, 2021, Accessed 3 March 2022. Available from: https://www.register-iri.com/wp-content/uploads/A.115532.pdf.
[23]	S. Wang, R. Yan, X. Qu, Development of a non-parametric classifier: Effective identification, algorithm, and applications in port state control for maritime transportation, Transp. Res. Part B, 128 (2019), 129–157. https://doi.org/10.1016/j.trb.2019.07.017 doi: 10.1016/j.trb.2019.07.017
[24]	L. Zhang, L. Guan, D. Z. Long, H. Shen, H. Tang, Who is better off by selling extended warranties in the supply chain: the manufacturer, the retailer, or both?, Ann. Oper. Res., 2020 (2020), 1–27. https://doi.org/10.1007/s10479-020-03728-z doi: 10.1007/s10479-020-03728-z
[25]	Tokyo MoU, List of Tokyo MoU deficiency codes, Accessed 28 October 2018. Available from: http://www.tokyo-mou.org/publications/tokyo_mou_deficiency_codes.php.
[26]	Tokyo MoU, Annual report on port state control in the Asia-Pacific region 2019, Accessed 17 July 2020. Available from: http://www.tokyo-mou.org/doc/ANN19-f.pdf.
[27]	R. Xu, Q. Lu, W. Li, K. X. Li, H. Zheng, A risk assessment system for improving port state control inspection, in Proceedings of 2007 International Conference on Machine Learning and Cybernetics, (2007), 818–823. https://doi.org/10.1109/ICMLC.2007.4370255
[28]	R. Xu, Q. Lu, K. X. Li, W. Li, Web mining for improving risk assessment in port state control inspection, in Proceedings of 2007 International Conference on Natural Language Processing and Knowledge Engineering, (2007), 427–434. https://doi.org/10.1109/NLPKE.2007.4368066
[29]	Z. Gao, G. Lu, M. Liu, M. Cui, A novel risk assessment system for port state control inspection, in Proceedings of 2008 IEEE International Conference on Intelligence and Security Informatics, (2008), 242–244.
[30]	R. Yan, S. Wang, J. Cao, D. Sun, Shipping domain knowledge informed prediction and optimization in port state control, Transp. Res. Part B Methodol., 149 (2021), 52–78. https://doi.org/10.1016/j.trb.2021.05.003 doi: 10.1016/j.trb.2021.05.003
[31]	R. Yan, S. Wang, K. Fagerholt, A semi-"smart predict then optimize" (semi-SPO) method for efficient ship inspection, Transp. Res. Part B Methodol., 142 (2020), 100–125. https://doi.org/10.1016/j.trb.2020.09.014 doi: 10.1016/j.trb.2020.09.014
[32]	Z. Yang, Z. Yang, J. Yin, Realising advanced risk-based port state control inspection using data-driven Bayesian networks, Transp. Res. Part A, 110 (2018), 38–56. https://doi.org/10.1016/j.tra.2018.01.033 doi: 10.1016/j.tra.2018.01.033
[33]	R. Yan, S. Wang, C. Peng, An artificial intelligence model considering data imbalance for ship selection in port state control based on detention probabilities, J. Comput. Sci., 48 (2021), 101257. https://doi.org/10.1016/j.jocs.2020.101257 doi: 10.1016/j.jocs.2020.101257
[34]	S. Wu, X. Chen, C. Shi, J. Fu, Y. Yan, S. Wang, Ship detention prediction via feature selection scheme and support vector machine (SVM), Marit. Policy Manage., 2021 (2021), 1–16. https://doi.org/10.1080/03088839.2021.1875141 doi: 10.1080/03088839.2021.1875141
[35]	F. Liu, K. Ting, Z. Zhou, Isolation forest, in Proceedings of 2008 Eighth IEEE International Conference on Data Mining, (2008), 413–422. https://doi.org/10.1109/ICDM.2008.17
[36]	F. Liu, K. Ting, Z. Zhou, Isolation-based anomaly detection, ACM Trans. Knowl. Discovery Data, 6 (2012), 1–39. https://doi.org/10.1145/2133360.2133363 doi: 10.1145/2133360.2133363
[37]	S. Lundberg, S. Lee, A unified approach to interpreting model predictions, Adv. Neural Inf. Process. Syst., 30 (2017).
[38]	Tokyo MoU, Annual report on port state control in the Asia-Pacific region 2017, Accessed 27 October 2018. Available from: http://www.tokyo-mou.org/doc/ANN17-f.pdf.
[39]	Tokyo MoU, Annual report on port state control in the Asia-Pacific region 2018, Accessed 23 August 2019. Available from: http://www.tokyo-mou.org/doc/ANN18-f.pdf.

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