Modelling ships main and auxiliary engine powers with regression-based machine learning algorithms

• Autorzy: Okumuş Fatih , Ekmekçioğlu Araks , Kara Selin Soner

Identyfikatory

DOI

10.2478/pomr-2021-0008

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

machine learning; regression; ship emissions; engine power; prediction

• Wydawca: Gdańsk University of Technology, Faculty of Ocean Engineering & Ship Technology
• Czasopismo: Polish Maritime Research
• Rocznik: 2021
• Tom: nr 1
• Strony: 83--96
• Opis fizyczny: Bibliogr. 21 poz., rys., tab.

Twórcy

autor

Okumuş Fatih

• Yildiz Technical University Besiktas, 34000 Istanbul Turkey

autor

Ekmekçioğlu Araks

• Yildiz Technical University Besiktas, 34000 Istanbul Turkey

autor

Kara Selin Soner

• Yildiz Technical University Besiktas, 34000 Istanbul Turkey

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).