Original scientific paper
https://doi.org/10.7225/toms.v13.n02.001
Identifying Factors of Dynamic Positioning Incidents through Association Rule Mining
Tugfan Sahin
; Istanbul Technical University, Graduate School, Department of Maritime Transport Engineering, Istanbul, Turkiye
*
Pelin Bolat
; Istanbul Technical University, Maritime Faculty, Department of Maritime Transport and Management Engineering, Istanbul, Turkiye
* Corresponding author.
Abstract
Accidents in the offshore industry can have severe repercussions for people, cargo, vessels, and the environment, making maritime safety a crucial concern. Dynamic positioning incidents, particularly those involving loss of position, represent a significant risk. This study employs association rule mining to analyze DP incident data, leveraging its strength in discovering robust associations. Using the Apriori algorithm, the analysis identifies strong association rules for loss of position (drift-off, drive-off) and loss of redundancy situations. The findings reveal event-related variables and potential causal relationships, providing insights and guidance for reducing the risk and occurrence of future DP incidents through stringent and targeted safety measures.
Keywords
Dynamic positioning incident; Data mining; Apriori algorithm; Association rule mining; Offshore; DPO training
Hrčak ID:
321851
URI
Publication date:
21.10.2024.
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