Ship speed prediction based on machine learning for efficient shipping operation
Ship speed prediction based on machine learning for efficient shipping operation
Optimizing ship operational performance has generated considerable research interest recently to reduce fuel consumption and its associated cost and emissions. One of the key factors to optimize ship design and operation is an accurate prediction of ship speed due to its significant influence on the ship operational efficiency. Traditional methods of ship speed estimation include theoretical calculations, numerical modeling, simulation, or experimental work which can be expensive, time-consuming, have limitations and uncertainties, or it cannot be applied to ships under different operational conditions. Therefore, in this study, a data-driven machine learning approach is investigated for ship speed prediction through regression utilizing a high-quality publicly-accessible ship operational dataset of the ‘M/S Smyril’ ferry. Employed regression algorithms include linear regression, regression trees with different sizes, regression trees ensembles, Gaussian process regression, and support vector machines using different covariance functions implemented in MATLAB and compared in terms of speed prediction accuracy. A comprehensive data preprocessing pipeline of operational features selection, extraction, engineering and scaling is also proposed. Moreover, cross validation, sensitivity analyses, correlation analyses, and numerical simulations are performed. It has been demonstrated that the proposed approach can provide accurate prediction of ship speed under real operational conditions and help in optimizing ship operational parameters.
MATLAB, Machine learning, Regression, Ship energy efficiency, Ship speed prediction
Bassam, Ameen
d9131851-3fa2-441f-93a7-996fde2bcf33
Phillips, Alexander
f565b1da-6881-4e2a-8729-c082b869028f
Turnock, Stephen
d6442f5c-d9af-4fdb-8406-7c79a92b26ce
Wilson, Philip
8307fa11-5d5e-47f6-9961-9d43767afa00
1 February 2022
Bassam, Ameen
d9131851-3fa2-441f-93a7-996fde2bcf33
Phillips, Alexander
f565b1da-6881-4e2a-8729-c082b869028f
Turnock, Stephen
d6442f5c-d9af-4fdb-8406-7c79a92b26ce
Wilson, Philip
8307fa11-5d5e-47f6-9961-9d43767afa00
Bassam, Ameen, Phillips, Alexander, Turnock, Stephen and Wilson, Philip
(2022)
Ship speed prediction based on machine learning for efficient shipping operation.
Ocean Engineering, 245 (1), [110449].
(doi:10.1016/j.oceaneng.2021.110449).
Abstract
Optimizing ship operational performance has generated considerable research interest recently to reduce fuel consumption and its associated cost and emissions. One of the key factors to optimize ship design and operation is an accurate prediction of ship speed due to its significant influence on the ship operational efficiency. Traditional methods of ship speed estimation include theoretical calculations, numerical modeling, simulation, or experimental work which can be expensive, time-consuming, have limitations and uncertainties, or it cannot be applied to ships under different operational conditions. Therefore, in this study, a data-driven machine learning approach is investigated for ship speed prediction through regression utilizing a high-quality publicly-accessible ship operational dataset of the ‘M/S Smyril’ ferry. Employed regression algorithms include linear regression, regression trees with different sizes, regression trees ensembles, Gaussian process regression, and support vector machines using different covariance functions implemented in MATLAB and compared in terms of speed prediction accuracy. A comprehensive data preprocessing pipeline of operational features selection, extraction, engineering and scaling is also proposed. Moreover, cross validation, sensitivity analyses, correlation analyses, and numerical simulations are performed. It has been demonstrated that the proposed approach can provide accurate prediction of ship speed under real operational conditions and help in optimizing ship operational parameters.
Text
speedprediction
- Accepted Manuscript
More information
Accepted/In Press date: 20 December 2021
Published date: 1 February 2022
Additional Information:
Funding Information:
The authors would like to thank Jóan Petur Petersen for the assistance and the provision of data utilized in this study.
Publisher Copyright:
© 2022 Elsevier Ltd
Keywords:
MATLAB, Machine learning, Regression, Ship energy efficiency, Ship speed prediction
Identifiers
Local EPrints ID: 453289
URI: http://eprints.soton.ac.uk/id/eprint/453289
ISSN: 0029-8018
PURE UUID: 732d2f60-49f9-4117-b1c5-0ccc0ef8e689
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Date deposited: 12 Jan 2022 17:34
Last modified: 17 Mar 2024 07:02
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Author:
Ameen Bassam
Author:
Alexander Phillips
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