Ship detention prediction via feature selection scheme and support vector machine (SVM)

Shubo Wu, Xinqiang Chen, Chaojian Shi, Junjie Fu, Ying Yan and Shengzheng Wang

Maritime Policy & Management, 2022, vol. 49, issue 1, 140-153

Abstract: Ship detention decision plays a key role in port state control (PSC) inspection process, which is compactly related to navigation safety and maritime environmental protection. Many focuses were paid to exploit intrinsic relationship among ship attributes (ship age, type, etc.), detention events and typical ship deficiencies. It is noted that many ship detention prediction frameworks were implemented considering single type of factors regardless of internal relationship between ship crucial deficiencies and ship attributes. To address the issue, we proposed a support vector machine (SVM) based framework to exploit crucial ship deficiencies, and thus forecast the probability of ship detention event. Firstly, we design a feature selection scheme to determine ship fatal deficiency types by exploring historical PSC inspection data. Secondly, we predict the ship detention event via conventional support vector machine (SVM) with support of ship feature selection outputs. Thirdly, we verify the proposed framework performance by predicting ship detention event from historical PSC data, which is quantified with the indicators of accuracy ($$Acc$$Acc) and area under ROC curve ($$AUC$$AUC). The research findings help PSC officials easily identify fatal ship deficiencies, and thus make more reasonable ship detention decision in real-world PSC activity.

Date: 2022
References: Add references at CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://hdl.handle.net/10.1080/03088839.2021.1875141 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:marpmg:v:49:y:2022:i:1:p:140-153

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TMPM20

DOI: 10.1080/03088839.2021.1875141

Access Statistics for this article

Maritime Policy & Management is currently edited by Dr Kevin Li and Heather Leggate McLaughlin

More articles in Maritime Policy & Management from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().