Prediction of ship's speed through ground using the previous voyage's drift speed

• Autorzy: Yamane D. , Kano T.

Identyfikatory

DOI

10.12716/1001.17.01.13

• Języki publikacji: EN

Abstrakty

EN

In recent years, 'weather routing' has been attracting increasing attention as a means of reducing costs and environmental impact. In order to achieve high-quality weather routing, it is important to accurately predict the ship's speed through ground during a voyage from ship control variables and predicted data on weather and sea conditions. Because sea condition forecasts are difficult to produce in-house, external data is often used, but there is a problem that the accuracy of sea condition forecasts is not sufficient and it is impossible to improve the accuracy of the forecasts because the data is external. In this study, we propose a machine learning method for predicting speed through ground by considering the actual values of the previous voyage’s drift speed for ships that regularly operate on the same route, such as ferries. Experimental results showed that this method improves the prediction performance of ship’s speed through ground.

Słowa kluczowe

EN

machine learning; weather routing; ship's speed estimation; drift speed; algorithm; route planning; tidal current

• Wydawca: Faculty of Navigation, Gdynia Maritime University
• Czasopismo: TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
• Rocznik: 2023
• Tom: Vol. 17 No. 1
• Strony: 129--137
• Opis fizyczny: Bibliogr. 16 poz., rys., tab.

Twórcy

autor

Yamane D.

• NPO Marine Technologist, Tokyo, Japan

autor

Kano T.

• NPO Marine Technologist, Tokyo, Japan

Bibliografia

• [1] W.Laura, R.Anisa, W.Mareike and J.Carlos, “Modeling and optimization algorithms in ship weather routing,” International journal of e-navigation and maritime economy, vol.4, pp.31-45, 2016.
• [2] J.Szlapczynska, “Multi-objective weather routing with customised criteria and constraints,” The journal of navigation, vol.68, pp.338-354, 2015.
• [3] U.Hollenbach , “Estimating resistance and propulsion for single-screw and twin screw ships.,” International conference on computer applications in shipbuilding, vol.2, pp 237–250, 1999.
• [4] J.Holtrop and G.G.J.Mennen, “An approximate power prediction method,” International shipbuilding progress, vol.29, pp.166-170, 1982.
• [5] T.Wieslaw and R.Krzysztof, “Applying artificial neural networks for modelling ship speed and fuel consumption,” Neural computing and applications, vol.32, pp.17379-17395, 2020.
• [6] R.Frank, “The perceptron: a probabilistic model for information storage and organization in the brain.,” Psychological review, vol.65, pp.386-408, 1958.
• [7] K.Guolin, M.Qi, F.Thomas, W.Taifeng, C.Wei, M.Weidong, Y.Qiwei, L.Tie-Yan, “LightGBM: a highly efficient gradient boosting decision tree,” Advances in neural information processing systems, vol.30, pp. 3149–3157, 2017.
• [8] K.Sato and T.Kano, “Eco-shipping project with speed planning system for japanese coastal ships,” Scientific journals of the maritime university of szczecin, vol.46, pp.147-154, 2016.
• [9] K.Konstantina, P.E.Themis, P.E.Konstantinos, V.K.Michails and I.F.Dimitrios, “Machine learning applications in cancer prognosis and prediction,” Computational and structural biotechnology Journal, vol.13, pp.8-17, 2015.
• [10] S.Jonathan, R.G.M. Mário, B.Silvana and A.L.M.Miguel, “Recent advances and applications of machine learning in solid-state materials science,” npj computational materials, vol.5, 2019.
• [11] E. Bal Beşikçi, O. Arslan, O. Turan and A.I. Ölçer, “An artificial neural network based decision support system for energy efficient ship operations,” Computers & operations research, vol.66, pp. 393-401, 2016.
• [12] E.M.Sara, B.Loubna, C.Stéphane and B.Abdelaziz, “Deep learning-based ship speed prediction for intelligent maritime traffic management,” Journal of marine science and engineering, vol.11, 2023.
• [13] C.Tianqi and G.Carlos, “XGBoost: A scalable tree boosting system,” Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp.785-794, 2016.
• [14] K.J.Yoon, L.H.Seok and O.J.Seok, “Study on prediction of ship’s power using light GBM and XGBoost,” Journal of advanced marine engineering and technology, vol.44, pp.174-180, 2020.
• [15] G.Léo, O.Edouard and V.Gaël, “Why do tree-based models still outperform deep learning on tabular data?,” arXiv, 2022.
• [16] B.James, B. Remi, B.Yoshua and K.Balazs, “Algorithms for hyper-parameter optimization,” Proceedings of the 24th international conference on neural information processing systems, pp.2546-2554, 2011.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).