Machine learning in sustainable ship design and operation: A review

The shipping industry faces a large challenge as it needs to significantly lower the amounts of Green House Gas emissions. Traditionally, reducing fuel consumption for ships has been achieved during design stage and, after building ship, through optimisation ship operations. In recent years, efficiency improvements using Machine Learning (ML) methods are quickly progressing, facilitated by available data from remote sensing, experiments and high-fidelity simulations. have successfully applied extract intricate empirical rules that can reduce emissions thereby helping achieve green shipping. This article presents an overview applying ML techniques enhance ships’ sustainability. work covers fundamentals applications in relevant areas: design, operational performance, voyage planning. Suitable approaches analysed compared on scenario basis, with their space also discussed. Meanwhile, reminder is given many inherent uncertainties hence should be used caution.