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Offshore Crew Boat Sailing Time Forecast using Regression Models

机译:使用回归模型预测海上乘员航行时间

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In this study, the relationship between total travelling time and the main interested factors which were distance, boat speed, wave height, wave direction, wind speed, and wind direction was investigated through several regression models (1st order, 2nd order, interaction, and combined model) using two different data sets of 4-hr average and 4-hr split data were used to find the optimal model for two types of boats, boat A and boat B. The performance of the forecasting models was evaluated using adjusted R-squared and MAPE. The 4-hr split data type was found to significantly improve forecasting more than 4-hr average data. Boat A's equation obtained the highest %R-sq(adj) of 92.86%, lowest MAPE of 4.4% with 86.27% decrease in MAPE from original equation for combined model. Furthermore, the combined boat's equation with combined model case provided the secondly high in forecasting performance of 88.34% of R-sq(adj), 8.73% of MAPE, and 77.79% decrease in MAPE. Hence, combined boat's equation is selected for AA Company to forecast the total sailing time since it provides high forecasting performance and is more convenient to use.
机译:在这项研究中,通过几种回归模型研究了总旅行时间与主要感兴趣的因素之间的关系,这些主要因素包括距离,船速,波浪高度,波浪方向,风速和风向(1 st 订单2 nd 顺序,交互和组合模型),使用两个不同的数据集(平均4小时和4小时分割数据)来找到两种类型的船(船A和船B)的最佳模型。预测模型的性能为使用调整后的R平方和MAPE进行评估。发现4小时分割数据类型比4小时平均数据显着改善了预测。与组合模型的原始方程相比,Boat A的方程式的最高%R-sq(adj)为92.86%,最低的MAPE为4.4%,MAPE降低了86.27%。此外,结合船模方程和结合模型的情况提供了第二高的预测性能,R-sq(adj)为88.34%,MAPE为8.73%,MAPE降低了77.79%。因此,AA公司选择组合船方程来预测总航行时间,因为它具有较高的预测性能并且更易于使用。

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