Pregled bibliografske jedinice broj: 1268485
Estimation of Tanker Ships’ Lightship Displacement Using Multiple Linear Regression and XGBoost Machine Learning
Estimation of Tanker Ships’ Lightship Displacement Using Multiple Linear Regression and XGBoost Machine Learning // Journal of marine science and engineering, 11 (2023), 5; 961, 17 doi:10.3390/jmse11050961 (međunarodna recenzija, članak, znanstveni)
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Naslov
Estimation of Tanker Ships’ Lightship Displacement
Using Multiple Linear Regression and XGBoost
Machine Learning
Autori
Frančić, Vlado ; Hasanspahić, Nermin ; Mandušić, Mario ; Strabić, Marko
Izvornik
Journal of marine science and engineering (2077-1312) 11
(2023), 5;
961, 17
Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni
Ključne riječi
oil tanker ; lightship displacement ; length overall ; breadth ; machine learning ; XGBoost
Sažetak
It is of the utmost importance to accurately estimate different ships’ weights during their design stages. Additionally, lightship displacement (LD) data are not always easily accessible to shipping stakeholders, while other ships’ dimensions are within hand’s reach (for example, through data from the online Automatic Identification System (AIS)). Therefore, determining lightship displacement might be a difficult task, and it is traditionally performed with the help of mathematical equations developed by shipbuilders. Distinct from the traditional approach, this study offers the possibility of employing machine learning methods to estimate lightship displacement weight as accurately as possible. This paper estimates oil tankers’ lightship displacement using two ships’ dimensions, length overall, and breadth. The dimensions of oil tanker ships were collected from the INTERTANKO Chartering Questionnaire Q88, available online, and, because of similar block coefficients, all tanker sizes were used for estimation. Furthermore, multiple linear regression and extreme gradient boosting (XGBoost) machine learning methods were utilised to estimate lightship displacement. Results show that XGBoost and multiple linear regression machine learning methods provide similar results, and both could be powerful tools for estimating the lightship displacement of all types of ships.
Izvorni jezik
Engleski
Znanstvena područja
Brodogradnja, Tehnologija prometa i transport
POVEZANOST RADA
Ustanove:
Pomorski fakultet, Rijeka,
Sveučilište u Dubrovniku
Citiraj ovu publikaciju:
Časopis indeksira:
- Current Contents Connect (CCC)
- Web of Science Core Collection (WoSCC)
- Science Citation Index Expanded (SCI-EXP)
- SCI-EXP, SSCI i/ili A&HCI
- Scopus