Estimation of Tanker Ships' Lightship Displacement Using Multiple Linear Regression and XGBoost Machine Learning

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.