Predicting tanker main engine power using regression analysis and artificial neural networks

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dc.contributor.authorGunes, Umit
dc.contributor.authorBashan, Veysi
dc.contributor.authorOzsari, Ibrahim
dc.contributor.authorKarakurt, Asim Sinan
dc.date.accessioned2026-02-12T21:05:40Z
dc.date.available2026-02-12T21:05:40Z
dc.date.issued2023
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractThe purpose-oriented design and planning of ships is maintained throughout production. Outer form of ship equipment starts with the steel construction process. The outer body production process moves ahead with painting, quality control tests, and bureaucratic procedures. In accordance with all these form and block operations, choosing a main engine suitable for all other technical parameters is vital, especially regarding ship speed and the amount of cargo it will carry. As a result, estimating main engine power is attempted with the help of artificial neural network (ANN) and regression analyses by considering a ship's technical parameters (e.g., draught, depth, deadweight tonnage [DWT], gross tonnage [GT], and engine power). This study conducts regression and ANN analyses over 836 tanker ships from the Marine Traffic database to predict main engine power using input parameters (deadweight (DWT), Length (L), Breadth (B), and gross ton (GT) values). The regression analyses show Model7 to perform the best approximation with a determination value = 0.827 usable for estimating main engine power. After all the examinations, a very accomplished result of 0.98047 was additionally obtained from the ANN analysis. The study makes beneficial and innovative contributions to predicting tankers' required main engine power.
dc.identifier.doi10.14744/sigma.2023.00029
dc.identifier.endpage225
dc.identifier.issn1304-7205
dc.identifier.issn1304-7191
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85147506525
dc.identifier.scopusqualityQ4
dc.identifier.startpage216
dc.identifier.urihttps://doi.org/10.14744/sigma.2023.00029
dc.identifier.urihttps://hdl.handle.net/20.500.12885/7059
dc.identifier.volume41
dc.identifier.wosWOS:000992743000001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherYildiz Technical Univ
dc.relation.ispartofSigma Journal of Engineering and Natural Sciences-Sigma Muhendislik Ve Fen Bilimleri Dergisi
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WoS_20260212
dc.subjectArtificial Neural Network
dc.subjectANN
dc.subjectShip
dc.subjectMain Engine
dc.subjectPower
dc.subjectRegression Analysis
dc.titlePredicting tanker main engine power using regression analysis and artificial neural networks
dc.typeArticle

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