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http://dx.doi.org/10.3744/SNAK.2020.57.6.312

Prediction of Residual Resistance Coefficient of Low-Speed Full Ships Using Hull Form Variables and Machine Learning Approaches  

Kim, Yoo-Chul (Korea Research Institute of Ships and Ocean Engineering (KRISO))
Yang, Kyung-Kyu (Department of Naval Architecture and Ocean Engineering, Chungnam National University)
Kim, Myung-Soo (Korea Research Institute of Ships and Ocean Engineering (KRISO))
Lee, Young-Yeon (Korea Research Institute of Ships and Ocean Engineering (KRISO))
Kim, Kwang-Soo (Korea Research Institute of Ships and Ocean Engineering (KRISO))
Publication Information
Journal of the Society of Naval Architects of Korea / v.57, no.6, 2020 , pp. 312-321 More about this Journal
Abstract
In this study, machine learning techniques were applied to predict the residual resistance coefficient (Cr) of low-speed full ships. The used machine learning methods are Ridge regression, support vector regression, random forest, neural network and their ensemble model. 19 hull form variables were used as input variables for machine learning methods. The hull form variables and Cr data obtained from 139 hull forms of KRISO database were used in analysis. 80 % of the total data were used as training models and the rest as validation. Some non-linear models showed the overfitted results and the ensemble model showed better results than others.
Keywords
Machine learning; Cr prediction; Low-speed full ship; Hull form variables; Regression;
Citations & Related Records
Times Cited By KSCI : 7  (Citation Analysis)
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