MODELING OF GENERAL CARGO SHIP’S MAIN ENGINE POWERS WITH REGRESSION BASED MACHINE LEARNING ALGORITHMS: COMPARATIVE RESEARCH

Authors : Fatih OKUMUŞ, Araks EKMEKÇİOĞLU

Pages : 1-8

Doi:10.47512/meujmaf.923874

View : 37 | Download : 13

Publication Date : 2021-06-30

Article Type : Research Paper

Abstract :This study, which allows estimating main engine power of new ships based on data from general cargo ships, consists of a series of mathematical relationships. Thanks to these mathematical relationships, it can be predicted main engine power according to length insert ignore into journalissuearticles values(L);, gross tonnage insert ignore into journalissuearticles values(GT); and age of a general cargo ship. In this study, polynomial regression, K-Nearest Neighbors insert ignore into journalissuearticles values(KNN); regression and Gradient Boosting Machine insert ignore into journalissuearticles values(GBM); regression algorithms are used. By this means the relationships presented here, it is aimed to build ships that are environmentally friendly and can be sustained at a lower cost by using the main engine power of the new ships with high accuracy. In addition, the relationships presented here provide validation for computational fluid dynamics insert ignore into journalissuearticles values(CFDs); and other studies with empirical statements. As a result of the study, polynomial regression gives similar results with other studies in the literature. We also concluded that while KNN regression yields fast results, GBM regression algorithm provides more accurate solutions to estimate the ship`s main engine power.
Keywords : Machine learning, Regression algorithm, General cargo ship, Engine power, Prediction

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