Assessing Ships' Environmental Performance Using Machine Learning

Environmental performance of ships is a critical factor in the shipping industry due to evolving climate change and the respective regulations imposed by authorities all over the world. As shipping moves towards digitization, a large amount of ships' environmental performance-related data, collected during ships' voyages, provide opportunities to develop and enhance data-driven performance models by using different machine learning algorithms. This paper introduces new indices of ships' environmental performance using machine learning techniques. The new indices are produced by combining clustering algorithms as well as principal component analysis. Based on the analysis of the data (14 variables with operational and design characteristics), the ships are divided into four clusters based on the new suggested indices. These clusters categorize the ships according to their physical dimensions, operating region, and operational environmental efficiency, offering insight into the distinctive traits of each cluster.