Citation

Fu J, Chen X, Wu S, Shi C, Wu H, Zhao J, Xiong P. PLoS One 2020; 15(2): e0229211.

Affiliation

School of Information Engineering, Nanchang University, Nanchang, China.

Copyright

(Copyright © 2020, Public Library of Science)

DOI

10.1371/journal.pone.0229211

PMID

32084200

Abstract

Early warning on the ship deficiency is crucial for enhancing maritime safety, improving maritime traffic efficiency, reducing ship fuel consumption, etc. Previous studies focused on the ship deficiency exploration by mining the relationships between the ship physical deficiencies and the port state control (PSC) inspection results with statistical models. Less attention was paid to discovering the correlation rules among various parent ship deficiencies and subcategories. To address the issue, we proposed an improved Apriori model to explore the intrinsic mutual correlations among the ship deficiencies from the PSC inspection dataset. Four typical ship property indicators (i.e., ship type, age, deadweight and gross tonnage) were introduced to analyze the correlations for the ship parent deficiency categories and subcategories. The findings of our research can provide basic guidelines for PSC inspections to improve the ship inspection efficiency and maritime safety.

Language: en