Big data analytics and machine learning of harbour craft vessels to achieve fuel efficiency : a review

Tay, Zhi Yung and Hadi, Januwar and Chow, Favian and Loh, De Jin and Konovessis, Dimitrios (2021) Big data analytics and machine learning of harbour craft vessels to achieve fuel efficiency : a review. Journal of Marine Science and Engineering, 9 (12). 1351. ISSN 2077-1312 (https://doi.org/10.3390/jmse9121351)

[thumbnail of Tay-etal-JMSE-2021-Big-data-analytics-and-machine-learning-of-harbour-craft]
Preview
 Text. Filename: Tay_etal_JMSE_2021_Big_data_analytics_and_machine_learning_of_harbour_craft.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo
Download (8MB)| Preview

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.

CORE (COnnecting REpositories)