Artificial neural network based prediction of ship speed under operating conditions for operational optimization. (15th June 2023)

• Record Type: Journal Article
• Authors: Bassam, Ameen M.; Phillips, Alexander B.; Turnock, Stephen R.; Wilson, Philip A.
• Is Part Of: Ocean engineering. Volume 278(2023)
• Publication Date: 2023-06-15
• Subjects: Ship speed prediction -- Data-driven model -- Machine learning -- Artificial neural network -- MATLAB; Ocean engineering -- Periodicals; Ocean engineering; Periodicals; 620.4162
• DOI: 10.1016/j.oceaneng.2023.114613
• Languages: English
• ISSNs: 0029-8018

Abstract

Abstract: 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. Highlights: Several ANN architectures for ship speed prediction are compared utilizing real data. Increasing data sample size can improve the correlation stability and uncertainty. Increasing data sample size results in better ANN training and prediction. The number of ANN training epochs did not affect the prediction accuracy much. Deep ANN with two hidden layers can accurately predict ship speed in operation.