A machine learning-based method for prediction of ship performance in ice: Part I. ice resistance

Abstract

This article focuses on design of an Artificial Neural Network (ANN) model to estimate ship resistance in ice-covered water by using suitable ship and ice parameters. In order to develop a reliable model, as much ice resistance test data as from the ship sea trials and model test measurements are collected to train the neural network. Different features (ship design parameters and ice mechanic properties) are explored to find a suitable combination of input features. Several algorithms are tested and compared to select a good model for resistance prediction. It turns out that seven features and the Radial Basis Function - Particle Swarm Optimization algorithm (RBF-PSO) can provide a reasonable generalization model. This study shows that the ice resistance predicted by the ANN correlates well with the measured data. The model developed herein can be used as an ice resistance prediction tool with high accuracy compared to the conventional semi-empirical formulae used in polar ship design.