A Machine Learning Ship’s Speed Over Ground Prediction Model and Sailing Time Control Strategy

This paper proposes a machine learning–based ship speed over a ground prediction model, driven by the eXtreme Gradient Boosting (XGBoost) algorithm. The data set is acquired from a world-sailing chemical tanker with five years of full-scale measurements. The model is trained using encountered metocean environments and ship operation profiles in two scenarios: through propeller revolutions per minute (RPM) or propulsion power. This model is further combined with the particle swarm optimization algorithm to integrate a sailing time control method. It optimizes constant RPM or power operation strategy to meet the requirements of a fixed estimated time of arrival.