<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Based on data from seven different ship types, this paper provides mathematical relationships that allow us to estimate the main and auxiliary engine power of new ships. With these mathematical relationships we can estimate the power of the engine based on the ship’s length (L), gross tonnage (GT) and age. We developed these approaches using simple linear regression, polynomial regression, K-nearest neighbours (KNN) regression and gradient boosting machine (GBM) regression algorithms. The relationships presented here have a practical application: during the pre-parametric design of new ships, our mathematical relationships can be used to estimate the power of the engines so that more environmentally friendly ships may be built. In addition, with the machine learning methodology, the prediction of the main engine (ME) and auxiliary engine (AE) powers used in the numerical calculation of ship-based emissions provides data for researchers working on emission calculations. We conclude that the GBM regression algorithm provides more accurate solutions to estimate the main and auxiliary engine power of a ship than other algorithms used in the study.</p>
</abstract>

Based on data from seven different ship types, this paper provides mathematical relationships that allow us to estimate the main and auxiliary engine power of new ships. With these mathematical relationships we can estimate the power of the engine based on the ship’s length (L), gross tonnage (GT) and age. We developed these approaches using simple linear regression, polynomial regression, K-nearest neighbours (KNN) regression and gradient boosting machine (GBM) regression algorithms. The relationships presented here have a practical application: during the pre-parametric design of new ships, our mathematical relationships can be used to estimate the power of the engines so that more environmentally friendly ships may be built. In addition, with the machine learning methodology, the prediction of the main engine (ME) and auxiliary engine (AE) powers used in the numerical calculation of ship-based emissions provides data for researchers working on emission calculations. We conclude that the GBM regression algorithm provides more accurate solutions to estimate the main and auxiliary engine power of a ship than other algorithms used in the study.

Modelling Ships Main and Auxiliary Engine Powers with Regression-Based Machine Learning Algorithms

Polish Maritime Research

This work is licensed under the Creative Commons Attribution 4.0 International License.

Based on data from seven different ship types, this paper provides mathematical relationships that allow us to estimate the main and auxiliary engine power of...

Modelling Ships Main and Auxiliary Engine Powers with Regression-Based Machine Learning Algorithms

Online veröffentlicht: 30. Apr. 2021

Seitenbereich: 83 - 96

DOI: https://doi.org/10.2478/pomr-2021-0008

Schlüsselwörter
machine learning, regression, ship emissions, engine power, prediction

© 2021 Fatih Okumuş et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Modelling Ships Main and Auxiliary Engine Powers with Regression-Based Machine Learning Algorithms

Online veröffentlicht: 30. Apr. 2021

Seitenbereich: 83 - 96

DOI: https://doi.org/10.2478/pomr-2021-0008

Schlüsselwörtermachine learning, regression, ship emissions, engine power, prediction

© 2021 Fatih Okumuş et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
machine learning, regression, ship emissions, engine power, prediction