Artificial neural network based prediction of ship speed under operating conditions for operational optimization

Bassam, Ameen M.; Phillips, Alexander B.; Turnock, Stephen R.; Wilson, Philip A.. 2023 Artificial neural network based prediction of ship speed under operating conditions for operational optimization. Ocean Engineering, 278, 114613. 10.1016/j.oceaneng.2023.114613

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Official URL: http://dx.doi.org/10.1016/j.oceaneng.2023.114613

Abstract/Summary

Ship speed is one of the most fundamental parameters which influences ship design, the energy efficiency of its operation, and safety. Therefore, ship speed selection and prediction under various environmental and operational conditions are of great concern recently for optimizing ship design and operational performance. Among the different approaches that address the ship speed topic, data-driven methodologies and Artificial Neural Network (ANN) techniques are attracting widespread interest due to its efficiency, accuracy, robustness, flexibility, and fault tolerance. Consequently, this study investigates multiple ANN model sizes and architectures to determine the suitable network parameters for ship speed prediction. Thus, we have a good balance between the model’s prediction accuracy and computational complexity. For this study, a publicly-available high-quality operational dataset suitable for benchmarking the results is utilized. This analysis also includes the effect of the data quantity and sampling duration on the data correlation and the ANN performance. The results indicate that the proposed ANN model can accurately predict ship speed under real operational conditions with an error of less than 1 knot. Furthermore, it has been shown that the proposed model can help with the decision-making and optimization processes of voyages planning and execution.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.1016/j.oceaneng.2023.114613
ISSN:	00298018
Date made live:	05 Jun 2023 13:23 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/534761

Item Type:

Publication - Article

Digital Object Identifier (DOI):

10.1016/j.oceaneng.2023.114613

ISSN:

00298018

Date made live:

05 Jun 2023 13:23 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/534761