Association rule mining for identification of port state control patterns in Malaysian ports

Abstract

Port State Control (PSC) inspection data is used for determining the inspection pattern of PSC in Malaysia and identifying the relationship between the inspection place, flag state, number of deficiency, detention result, and ship risk profile. Based on 8,089 inspection reports from 2015 to 2019, the mining association rule is proposed as a learning approach due to its determination pattern in the information bank. The learning of association rules of PSC inspections is performed primarily on the Apriori Algorithm, in order to produce alluring rules. Inspection patterns of Malaysian ports revealed that flag state, ship risk profile, and inspection place generally lead to no detention result, as well as zero deficiency recorded on-board. The reported quantity of detention was significantly related to the high number of deficiencies raised for ships registered under blacklisted countries. Furthermore, the analysis of deficiency discovered the pattern of inspection at Malaysian ports is frequently related to zero and a low number of deficiencies raised by inspectors. Lastly, five major ports were selected for providing a useful rule to help PSC officers in organising an effective inspection plan. A similar approach can also be used for other ports beyond Malaysia for comparative analysis.

On the K-Means Clustering Model for Performance Enhancement of Port State Control

“…Chung et al [15] examined the correlations between ship features and flaws found during inspections (e.g., ship type, flag, and classification society). Additionally, Osman et al [16] used association rule mining techniques to examine PSC patterns in Malaysian ports. The location of the inspection, the flag state, the number of violations, the outcome of the detention, and the risk profile of the ship were all taken into account.…”

A bibliometric review on maritime inspection analysis: Current and future insights

“…When reviewing featured studies using the diagnostic analytics approach for maritime inspection analysis, information about the data used, the weaknesses and the superior aspects of articles are noticeable. Osman et al 47 suggested using PSC inspection data to determine the inspection layout of the PSC in Malaysia and to define the relationship between the inspection location, flag status, number of deficiencies, custody result, and vessel risk profile, using the association rule with Apriori algorithm. In addition, they discuss the information from this study that can be used to develop the vessel targeting system to help the inspector choose the ship for inspection.…”

“…Used method on papers Diagnostic analytics Apriori algorithm 47,[49][50][51] Clustering analysis 7 Discriminant analysis 48 Table 8. Predictive analytic approach methods used on research papers.…”

Using Bayesian network-based TOPSIS to aid dynamic port state control detention risk control decision

“…The findings provided countermeasures and can be used as a reference by ship management personnel during the corresponding PSC inspection to reduce the detention rate of ships, improve working efficiency of staff members, and reduce the adverse influences brought by substandard vessels. Similarly, Osman et al (2020) used the same approach to analyse the inspection pattern in Malaysian port and provide a useful rule to help PSC officers in organizing an effective inspection plan.…”