Association rule learning to improve deficiency inspection in port state control

Chung, Wu-Hsun; Kao, Sheng-Long; Chang, Chih Chung; Yuan, Chien-Chung

doi:10.1080/03088839.2019.1688877

Cited by 35 publications

(15 citation statements)

References 11 publications

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“…The correlation analysis has shown its success in exploring intrinsic feature exploration applications (including the maritime knowledge discovery). More specifically, previous studies suggested that ship deficiencies were highly related in triggering ship detention events according to the PSC detection reports [20]. In addition, to find the rules conducted in the PSC inspection activities, we interviewed many maritime practitioners (i.e., maritime officials) in the form of a questionnaire, which is consistent with literature reviewing results.…”

Section: Model Developmentmentioning

confidence: 86%

“…Stimulated by those results, we conducted a correlation analysis on mining the historical PSC inspection data. The Apriori relevant models have been employed to identify the crucial ship deficiencies from different ship detention cases, indicating favorable ship deficiency recognition results [20,21]. In that manner, the Apriori model is considered as an efficient model to tackle the challenge of exploiting intrinsic ship deficiency categories from the historical PSC dataset.…”

Section: Model Developmentmentioning

confidence: 99%

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Mining ship deficiency correlations from historical port state control (PSC) inspection data

Chen

et al. 2020

PLoS ONE

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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.

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Section: Model Developmentmentioning

confidence: 86%

Section: Model Developmentmentioning

confidence: 99%

Mining ship deficiency correlations from historical port state control (PSC) inspection data

Chen

et al. 2020

PLoS ONE

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“…Similar work had also been carried out by (Emecen Kara and Oksas 2016) where the authors compared the performance of nine (9) PSC regions from 2012 to 2014 and discussed in detail the various types of PSC findings and the categories of ships that had been detained and subsequently proposed an effective inspection system. (Chung et al 2020) utilised the inspection data in Taiwan's ports and applied data mining to discover the correlation between the nature of deficiencies. In this work, the Apriori algorithm is used to identify the relationship between the deficiencies to provide the inspector with useful information in order to enhance the PSC inspection.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Osman

Chen

Tian

et al. 2020

Maritime Policy & Management

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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.

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“…The literature on Port State Control covers various topics such as the evaluation of the effectiveness of Port State Control regimes [1,9] or the use of past deficiencies to predict future risk or to guide inspectors to find more deficiencies [11][12][13][14] but the standard assumption remains that detention equals incident risk. This manuscript challenges this assumption and presents a methodology that treats incident risk and detention as two separate risk dimensions.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the presented approach allows for the combination of an automated data-driven part with qualitative aspects. Data-driven approaches have been proposed previously [1,[11][12][13][14] but exclude other components included here, such as accounting for two different risk dimensions and addition of inspection priority areas. The data-driven part here provides the risk profiles of individual vessels for eight vessel inspection priority risk areas and combines detention and incident (VSS) risk into one metric to enhance targeting.…”

Section: Introductionmentioning

confidence: 99%

Improved Strategies for the Maritime Industry to Target Vessels for Inspection and to Select Inspection Priority Areas

Knapp

Heij

2020

Safety

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Inspection authorities such as the Port State Control Memoranda of Understanding use different policies and targeting methods to select vessels for inspections and rely primarily on past inspection outcomes. One of the main goals of inspections is to improve the safety quality of vessels and to reduce the probability of future incidents. This study shows there is room for improvement in targeting vessels for inspections and in determining vessel-specific inspection priority areas (e.g., bridge management versus machinery related items). For the year 2018, sixty percent of vessels that experienced very serious or serious (VSS) incidents were not selected for inspection up to three months prior to the incident and forty percent of the vessels that were inspected still had incidents of which only four percent were detained. Furthermore, one can observe a very low correlation (−0.04) between the probabilities of detention and incidents (VSS) for the year 2018. The proposed approach treats detention and incident types as separate risk dimensions and evaluates seven targeting methods against random selection of vessels using empirical data for 2018. The analysis is based on three comprehensive data sets that cover the world fleet and shows potential gains (reduction of false negative events) of 14–27 percent compared to random selection. This can be further improved by adding eight inspection priority risk areas that help inspectors to focus inspections by providing insight in the individual risk profile of vessels. Policy makers can further customize the approach by classifying the risk of vessels into categories and by selecting inspection targets and benchmark samples. A small application example is provided to demonstrate feasibility of the proposed approach for policy makers and inspection authorities.

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Association rule learning to improve deficiency inspection in port state control

Cited by 35 publications

References 11 publications

Mining ship deficiency correlations from historical port state control (PSC) inspection data

Mining ship deficiency correlations from historical port state control (PSC) inspection data

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

Improved Strategies for the Maritime Industry to Target Vessels for Inspection and to Select Inspection Priority Areas

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