Big Data Analytics and Machine Learning of Harbour Craft Vessels to Achieve Fuel Efficiency: A Review

Abstract

The global greenhouse gas emitted from shipping activities is one of the factors contributing to global warming; thus, there is an urgent need to mitigate the adverse effect of climate change. One of the key strategies is to build a vibrant maritime industry with the use of innovation and digital technologies as well as intelligent systems. The digitization of the shipping industry not only provides a competitive edge to the shipping business model but also enhances ship operational and energy efficiency. This review paper focuses on the big data analytics and machine learning applied to harbour craft vessels with the aim to achieve fuel efficiency. The paper reviews the telemetry system requires for the digitalization of harbour craft vessels, its challenges in installation, the vessel monitoring and data transmission system. The commonly used methods for data cleaning are also presented. Last but not least, the paper considers two types of the machine learning systems, i.e., supervised and unsupervised machine learning systems. The multi-linear regression and hidden Markov model for supervised machine learning system and the artificial neural network, grey box model and long short-term memory model for unsupervised machine learning are discussed, and their pros and cons are presented.

An insight into the Application of AI in maritime and Logistics toward Sustainable Transportation

“…Machine learning (ML) to optimize cargo handling has considerable prospects for enhancing marine logistics efficiency, productivity, and safety. ML algorithms may enhance cargo handling procedures at ports and terminals by analyzing data from various sources, such as cargo manifests, vessel schedules, port infrastructure, and historical performance indicators [109]. One important ML use in cargo handling optimization is the automated scheduling and prioritizing of loading and unloading processes [110].…”

Self-labelling of tugboat operation using unsupervised machine learning and intensity indicator

“…The main activity of the tugboat consists of anchoring, assisting in large vessel docking, and piloting around the southern sea of Singapore. The purpose of collecting the operational data from the subject vessel was to conduct a research study in predicting the fuel rate to achieve fuel efficiency via data analytics and machine learning (Tay et al, 2021a(Tay et al, , 2021bHadi et al, 2022a).…”

Dynamic Failure Analysis of Ship Energy Systems Using an Adaptive Machine Learning Formalism

“…Further application of artificial intelligence in ship systems and offshore energy systems analysis has been demonstrated by the researchers (Arzaghi et al, 2020;Carroll et al, 2015;Hatti, 2020;Ossai et al, 2016;Zahraee et al, 2016) The increasing need for digitization in the marine industry and data mining have created more opportunities to develop advanced data science and machine learning techniques for the maritime. Recent works that explore the application of machine learning for marine energy system forecast and other aspects of the maritime operations are detailed in the referenced literature (Cheliotis et al, 2020;Kim et al, 2021;Peng et al, 2020;Planakis et al, 2022;Tay et al, 2021;Uyanık et al, 2020). For instance, Peng et al (2020) applied the machine learning formalism to predict ships' energy consumption at Port.…”

“…The result shows that deadweight tonnage, facilities efficiency, net tonnage and actual ship weight critically affect the amount of energy consumption of ships at Port. Similarly, Tay et al (2021) highlighted the pros and cons of applying machine learning techniques, such as artificial neural networks, hidden Markov model and Bayesian inference, etc., for ship energy efficiency prediction. However, there is no conclusive study on the application of these models for marine energy system failure prediction.…”