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This paper is dedicated to correcting the WAM/ICON numerical wave model predictions by reducing the residue between the model’s predictions and the actual buoy observations. The two parameters used in this paper are significant wave height and wind speed. The paper proposes two machine learning models to solve this task. Both models are multioutput models and correct the significant wave height and wind speed simultaneously. The first machine learning model is based on gradient boosted trees, which is trained to predict the residue between the model’s forecasts and the actual buoy observations using the other parameters predicted by the numerical model as inputs. This paper demonstrates that this model can significantly reduce errors for all used geographical locations. This paper also uses SHapley Additive exPlanation values to investigate the influence that the numerically predicted wave parameters have when the machine learning model predicts the residue. To design the second model, it is assumed that the residue can be modelled as noise added to the actual values. Therefore, this paper proposes to use the denoising autoencoder to remove this noise from the numerical model’s prediction. The results demonstrate that denoising autoencoders can remove the noise for the wind speed parameter, but their performance is poor for the significant wave height. This paper provides some explanations as to why this may happen.

This paper is dedicated to an overview of components of an onboard control system of an autonomous ship. This system controls and operates the ship. Therefore, this system needs to be able to analyze the ship’s state, predict its future development and analyze the consequences of its own decisions. The paper focuses on software aspects of the onboard control system, not the hardware. The paper provides an overview of technologies that can be used to implement the components of such a system responsible for planning new routes, handling the ship during the voyage, ensuring its seaworthiness and safety during the voyage, monitoring an autonomous ship from an onshore control centre, ensuring the robustness of the onboard control system, and collective operations of multiple autonomous ships. The paper describes benefits the maritime industry would gain from deploying some of the technologies developed for autonomous ships on ordinary, human-controlled ships. The paper also describes some challenges, especially in the field of automatic decision and reasoning, arising from the emergence of autonomous and smart ships. The main contribution of the paper is that it summarizes existing research in different areas of autonomous ship technology.