Ferry services that connect a huge number of islands and mainlands are vital transportation methods in several nations. However, a major disadvantage of ferry services is that they are crucially affected by weather conditions. Informing customers about regular ferry service operations is thus very important. With this in mind, the aim of this study was to predict whether ferry services can be provided in a timely manner through machine learning approaches with meteorological (6–48 h prior) and operation data sets. It was found that the random forest classifier achieved accuracy levels of 90.50% (6 h prior) and 88.78% (48 h prior) in predicting ferry services, which were greater than regulation-oriented determination. Both implications and limitations are presented based on the findings of this study.

中文翻译：

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使用机器学习通过综合气象数据预测轮渡服务

连接大量岛屿和大陆的渡轮服务是许多国家的重要交通方式。然而，渡轮服务的一个主要缺点是它们受天气条件的影响很大。因此，向客户通报定期渡轮服务的运营情况非常重要。考虑到这一点，本研究的目的是通过机器学习方法利用气象（6-48 小时前）和运营数据集来预测是否可以及时提供轮渡服务。结果发现，随机森林分类器在预测渡轮服务方面达到了 90.50%（6 小时前）和 88.78%（48 小时前）的准确率，高于以法规为导向的确定。根据本研究的结果提出了影响和局限性。