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

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 (L), gross tonnage (GT) and age of a general cargo ship. In this study, polynomial regression, K-Nearest Neighbors (KNN) regression and Gradient Boosting Machine (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 (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.