As tugboats interact very closely with ships in restricted waters, the possibility of accidents increases in these operations. Despite the high accident possibility, there is a gap in studies on tugboat accidents. This study aims to analyse accidents involving tugboats using data mining. For this purpose, a tugboat accidents dataset consisting of a total of 496 accident records for the period from 2008 to 2019 was collected. Logistic regression and decision tree algorithms were implemented to the dataset. The results revealed that tugboat propulsion type is the most important and influential factor in the severity of tugboat accidents. The inferences drawn from these results could be beneficial for tugboat operators and port authorities in enhancing their awareness of the factors affecting tugboat accidents. In addition, the outputs of this study can be a reference for management units in developing strategies for preventing tugboat accidents and can also be used in effective planning for practicable prevention programmes and practices.

中文翻译：

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决策树和逻辑回归分析探讨港口拖轮事故的影响因素

由于拖船在受限水域与船舶的相互作用非常密切，因此在这些操作中发生事故的可能性增加了。尽管发生事故的可能性很高，但对拖轮事故的研究仍然存在差距。本研究旨在使用数据挖掘分析涉及拖船的事故。为此，收集了一个拖船事故数据集，该数据集包含 2008 年至 2019 年期间的总共 496 条事故记录。对数据集实施了逻辑回归和决策树算法。结果表明，拖轮推进方式是影响拖轮事故严重程度的最重要和影响因素。从这些结果中得出的推论可能有利于拖船经营者和港口当局提高他们对影响拖船事故的因素的认识。此外，