Data-driven prediction of added-wave resistance on ships in oblique waves—A comparison between tree-based ensemble methods and artificial neural networks

The present paper implements machine learning methods for the prediction of the added-wave resistance of ships in head to beam wave conditions. The study is focused on non-linear regression algorithms namely Random Forests, Extreme Gradient Boosting Machines and Multilayer Perceptrons. The employed dataset is derived from results of three different potential flow methods covering a wide range of operational conditions and 18 hull forms in total. The rational data preprocessing makes up the core part of the paper having its focal point on practical application. Moreover, a rigorous hyperparameter study based on Bayesian optimization is conducted, and the validation of the final models for three case studies against numerical and experimental data as well as two established prediction techniques shows satisfactory generalization in case of the neural network. The tree-based ensemble methods, on the other hand, are not able to generalize sufficiently from the given parameter discretization of the underlying dataset.