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Use of tree based methods in ship performance monitoring under operating conditions

机译：基于树的方法在运行条件下的船舶性能监控中的使用

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Monitoring of operational efficiency in ship fleets is a complex maritime problem which requires an analytical approach in order to provide satisfactory solutions. Since the problem involves high-dimensional data, this paper develops tree-based modelling on bagging, random forest and bootstrap approach to analyse the ship performance under operational condition. To demonstrate the proposed model, the publicly accessible dataset for 254 trips derived from a particular designed acquisition system on-board ferry ship is utilised. In operational variable analysis on speed through water and fuel consumption, the bootstrap approach yields more accurate prediction rate than random forest and bagging. The proposed model is superior to the others such as ANN and GP applications in ship performance monitoring. Consequently, the tree based model adopting bagging, random forest, and boosting environment is capable of increasing the predictive performance during monitoring of ship performance in maritime industry. Beside its theoretical insight, the findings of the paper contribute ship management companies to monitor ship operational performance.
机译：监测船队的运营效率是一个复杂的海上问题，需要一种分析方法才能提供令人满意的解决方案。由于问题涉及高维数据，因此本文针对装袋，随机森林和自举法建立了基于树的建模方法，以分析船舶在运营条件下的性能。为了演示所提出的模型，利用了从一个特殊设计的船上渡轮采集系统获得的254个行程的公众可访问数据集。在对通过水和燃料消耗的速度进行的操作变量分析中，自举法比随机森林和装袋法产生更准确的预测率。所提出的模型在船舶性能监控方面优于其他模型，例如ANN和GP。因此，基于树的模型采用套袋，随机森林和提振环境，能够在监控海运业船舶性能的过程中提高预测性能。除了其理论上的洞察力之外，本文的发现还有助于船舶管理公司监测船舶的运营绩效。

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