Prediction of main particulars of container ships using artificial intelligence algorithms

The value of the main ship particulars are key values to determine during initial design of a vessel, but they can be complex to determine, as they depend on a large number of factors. The presented research attempts to model the main particulars: length between perpendiculars (LPP), length overall (LOA), modulated breadth (B), depth (D), draught (T), gross tonnage (GT), net tonnage (NT), deadweight (DWT), and engine power, using only key request factors: number of twenty-foot equivalent units (TEU) the vessel is expected to carry, and the expected speed of the ship (V). As this is a complex task, artificial intelligence (AI) techniques are applied to the dataset consisting of 250 container ships. Two modeling techniques are used: multilayer perceptron (MLP) and gradient boosted trees (GBT). The model hyperparameters are trained using a grid search procedure and evaluated using mean absolute percentage error (MAPE) and coefficient of determination (R2) in a 5-fold cross-validation scheme. The obtained results show that a quality model can be achieved using both techniques, except in the case of engine power for which a high-quality model has not been regressed. Models presented here can have practical application in the determination of the ship’s main particulars at the preliminary design stage. •A dataset describing the main particulars of container ships is collected.•The models are developed for the ship particulars using speed and TEU as inputs.•The modelling methods used are multilayer perceptron and gradient boosted trees.•All the models achieve satisfactory results, with the exception of engine power.