prikaz prve stranice dokumenta Determining residuary resistance per unit weight of displacement with Symbolic Regression and Gradient Boosted Tree algorithms
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Scientific paper - Original scientific paper
Determining residuary resistance per unit weight of displacement with Symbolic Regression and Gradient Boosted Tree algorithms
Pomorstvo, 35 (2021), 2; 275-284. https://doi.org/10.31217/p.35.2.11
Baressi Šegota, Sandi; Lorencin, Ivan; Šercer, Mario; Car, Zlatan

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Baressi Šegota, S., Lorencin, I., Šercer, M. & Car, Z. (2021). Determining residuary resistance per unit weight of displacement with Symbolic Regression and Gradient Boosted Tree algorithms. Pomorstvo, 35. (2), 275-284. doi: 10.31217/p.35.2.11

Baressi Šegota, Sandi, et al. "Determining residuary resistance per unit weight of displacement with Symbolic Regression and Gradient Boosted Tree algorithms." Pomorstvo, vol. 35, no. 2, 2021, pp. 275-284. https://doi.org/10.31217/p.35.2.11

Baressi Šegota, Sandi, Ivan Lorencin, Mario Šercer and Zlatan Car. "Determining residuary resistance per unit weight of displacement with Symbolic Regression and Gradient Boosted Tree algorithms." Pomorstvo 35, no. 2 (2021): 275-284. https://doi.org/10.31217/p.35.2.11

Baressi Šegota, S., et al. (2021) 'Determining residuary resistance per unit weight of displacement with Symbolic Regression and Gradient Boosted Tree algorithms', Pomorstvo, 35(2), pp. 275-284. doi: 10.31217/p.35.2.11

Baressi Šegota S, Lorencin I, Šercer M, Car Z. Determining residuary resistance per unit weight of displacement with Symbolic Regression and Gradient Boosted Tree algorithms. Pomorstvo [Internet]. 2021 December [cited 2024 June 29];35(2):275-284. doi: 10.31217/p.35.2.11

S. Baressi Šegota, I. Lorencin, M. Šercer and Z. Car, "Determining residuary resistance per unit weight of displacement with Symbolic Regression and Gradient Boosted Tree algorithms", Pomorstvo, vol. 35, no. 2, pp. 275-284, December 2021. [Online]. Available at: https://urn.nsk.hr/urn:nbn:hr:187:446153. [Accessed: 29 June 2024]

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Metadata
Title (english)Determining residuary resistance per unit weight of displacement with Symbolic Regression and Gradient Boosted Tree algorithms
AuthorSandi Baressi Šegota https://orcid.org/0000-0002-3015-1024
AuthorIvan Lorencin
AuthorMario Šercer https://orcid.org/0000-0002-4101-4092
AuthorZlatan Car https://orcid.org/0000-0003-2817-9252
Author's institutionUniversity of Rijeka
Faculty of Maritime Studies, Rijeka
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Traffic and Transport Technology
Abstract (english)
Determining the residuary resistance per unit weight of displacement is one of the key factors in the design of vessels. In this paper, the authors utilize two novel methods – Symbolic Regression (SR) and Gradient Boosted Trees (GBT) to achieve a model which can be used to calculate the value of residuary resistance per unit weight, of displacement from the longitudinal position of the center of buoyancy, prismatic coefficient, length-displacement ratio, beam-draught ratio, length-beam ratio, and Froude number. This data is given as results of 308 experiments provided as a part of a publicly available dataset. The results are evaluated using the coefficient of determination (R2) and Mean Absolute Percentage Error (MAPE). Pre-processing, in the shape of correlation analysis combined with variable elimination and variable scaling, is applied to the dataset. The results show that while both methods achieve regression results, the result of regression of SR is relatively poor in comparison to GBT. Both methods provide slightly poorer, but comparable results to previous research focussing on the use of “black-box” methods, such as neural networks. The elimination of variables does not show a high influence on the modeling performance in the presented case, while variable scaling does achieve better results compared to the models trained with the non-scaled dataset.
Keywords (english)
Languageenglish
Publication typeScientific paper - Original scientific paper
Publication statusPublished
Peer reviewPeer review
Publication versionPublished version
Journal titlePomorstvo
Numberingvol. 35, no. 2, pp. 275-284
p-ISSN1332-0718
e-ISSN1846-8438
DOIhttps://doi.org/10.31217/p.35.2.11
URN:NBNurn:nbn:hr:187:446153
Publication2021-12
Document URLhttps://hrcak.srce.hr/267183
Type of resourceText
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2022-02-25 14:14:26