Thanh bên bài viết
Số xuất bản:
Số 85 (2026): Số 85 (01/2026)
Chuyên mục:
KỸ THUẬT VÀ CÔNG NGHỆ HÀNG HẢI
DOI:
10.65154/jmst.951
Ngày xuất bản:
30/01/2026
Lượt xem
18
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16
Trích dẫn bài báo
Nguyen, M. Q., Vu, Q. H., & Nguyen, D. H. (2026). SHORT-TERM WAVE FORECASTING USING AI MODELS FOR THE OPERATION OF NAVIGATION CHANNELS AND SEAPORTS IN VIETNAM. Tạp chí Khoa học Công nghệ Hàng hải, 85, 87-97. https://doi.org/10.65154/jmst.951
Định dạng trích dẫn:
SHORT-TERM WAVE FORECASTING USING AI MODELS FOR THE OPERATION OF NAVIGATION CHANNELS AND SEAPORTS IN VIETNAM
Nội dung chính của bài viết
Tóm tắt
The paper presents an overview of artificial intelligence (AI) models, also known as artificial neural networks (ANNs), applied in short-term wave forecasting and ship motion prediction models under the influence of waves, serving the operation of navigation channels and seaports in Vietnam. The process is divided into two steps: first, AI models are used to forecast short-term waves. In this part, different AI-based wave forecasting models are analyzed and compared, and the most suitable model is selected for wave prediction. Then, the predicted waves are input into the ship motion prediction model as a basis for evaluating scenarios of ship entry and exit in the channel. The forecasting models developed in this study are based on datasets collected over many years in the Quảng Ninh coastal area and can be applied to the operation of Cái Lân, Vạn Ninh, and Cẩm Phả seaports. The computational results and analysis show that the application of AI models for short-term wave forecasting achieves very high accuracy. Among them, the Long Short-Term Memory (LSTM) model provides the most accurate results within the shortest computation time.
Từ khóa
Wave forecasting, AI model, ANNs, LSTM, Seaports, Navigation channels.
Chi tiết bài viết
Tài liệu tham khảo
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[34] Z. Jiang, Y. Ma, and W. Li (2024), A Data-Driven Method for Ship Motion Forecast, Journal of Marine Science and Engineering, Vol.12, No.2, p. 291.
[35] D. D’Agostino, A. Serani, F. Stern, and M. Diez (2022), Time-series forecasting for ships maneuvering in waves via recurrent-type neural networks, Journal of Ocean Engineering and Marine Energy, Vol.8, pp.479-487.
[36] J. M. J. Journée (2001), User manual of SEAWAY, Delft University of Technology, Delft, Netherlands.
[2] J. M. Giron-Sierra (2010), State-Of-The-Art of Wave Measurement for Ship Motion Prediction, IFAC Proceedings Volumes, Vol.43, No.20, pp.295-300.
DOI: 10.3182/20100915-3-DE-3008.00057.
[3] S. Kim, T. H. Tom, M. Takeda, and H. Mase (2021), A framework for transformation to nearshore wave from global wave data using machine learning techniques: Validation at the Port of Hitachinaka, Japan, Ocean Engineering, Vol.235, p. 109355.
[4] S. C. James and F. O'Donncha (2018), A machine learning framework to forecast wave conditions, Coastal Engineering, Vol.139, pp.1-10.
[5] J. Oh and K.-D. Suh (2018), Real-time forecasting of wave heights using EOF - wavelet - neural network hybrid model, Ocean Engineering, Vol.150, pp.48-59.
[6] T. H. A. Tom, A. Ikemoto, H. Mase, K. Kawasaki, M. Takeda, and S. Kim (2019), Wave Prediction in the Sea of Japan by Deep Learning Using Meteorological Data, Journal of Japan Society of Civil Engineers, Vol.75, No.2, pp.I_145-I_150.
[7] K. Khosravi and S. Heddam (2024), Near real-time significant wave height prediction along the coastline of Queensland using advanced hybrid machine learning models, International Journal of Environmental Science and Technology, Vol.21, pp.1-18.
[8] H. Salah and M. Elbessa (2024), Using Machine Learning Techniques to Predict Significant Wave Height Compared with Parametric Methods, Engineering and Applied Sciences, Vol.9, No.5, pp.106-128.
[9] T. Song, R. Han, F. Meng, J. W. W. Wei, and S. Peng (2022), A significant wave height prediction method based on deep learning combining the correlation between wind and wind waves, Frontiers in Marine Science, Vol.9, p. 1032845.
[10] N. P. Juan and V. N. Valdecantos (2022), Review of the application of Artificial Neural Networks in ocean engineering, Ocean Engineering, Vol.254, p. 111353.
[11] C. Jörges, C. Berkenbrink, H. Gottschalk, and B. Stumpe (2023), Spatial ocean wave height prediction with CNN mixed-data deep neural networks using random field simulated bathymetry, Ocean Engineering, Vol.275, p. 114131.
[12] C. Ni and X. Ma (2018), Prediction of Wave Power Generation Using a Convolutional Neural Network with Multiple Inputs, Energies, Vol.11, No.8, p. 2003.
[13] S. Mandal and N. Prabaharan (2006), Ocean wave forecasting using recurrent neural networks, Ocean Engineering, Vol.33, No.10, pp.1401-1410.
[14] T. Sadeghifar, M. N. Motlagh, M. T. Azad, and M. M. Mahdizadeh (2017), Coastal Wave Height Prediction using Recurrent Neural Networks (RNNs) in the South Caspian Sea, Marine Geodesy, Vol.40, No.6, pp.441-455.
[15] P. Abhigna, S. Jerritta, R. Srinivasan, and V. Rajendran (2017), Analysis of feed forward and recurrent neural networks in predicting the significant wave height at the moored buoys in Bay of Bengal, in Proc. Int. Conf. Commun. Signal Process. (ICCSP), Chennai, India, pp.1032-1036.
[16] Z.-F. Meng, Z. Chen, B. C. Khoo, and M. Zhang (2022), Long-time prediction of sea wave trains by LSTM machine learning method, Ocean Engineering, Vol.245, p. 110468.
[17] X. Zhang, Y. Li, S. Gao, and P. Ren (2021), Ocean Wave Height Series Prediction with Numerical Long Short-Term Memory, J. Mar. Sci. Eng., Vol.9, No.5, p. 514.
[18] B. J. Bethel, W. Sun, C. Dong, and D. Wang (2022), Forecasting hurricane-forced significant wave heights using a long short-term memory network in the Caribbean Sea, Ocean Science, Vol.18, No.2, pp.419-436.
[19] N. Bruneau, J. Polton, J. Williams, and J. Holt (2020), Estimation of global coastal sea level extremes using neural networks, Environmental Research Communications, Vol.2, No.5, p. 051009.
[20] H. Wang, J. Yin, and X. Wang (2020), Optimization of Wavelet Neural Network Model for Tide Prediction Based on Genetic Algorithm, in Proc. Chinese Control Decis. Conf. (CCDC), Hefei, China, pp.3205-3209.
[21] P. C. Deka and R. Prahlada (2012), Discrete wavelet neural network approach in significant wave height forecasting for multistep lead time, Ocean Engineering, Vol.43, pp.32-42.
[22] M. Özger (2010), Significant wave height forecasting using wavelet fuzzy logic approach, Ocean Engineering, Vol.37, No.16, pp.1443-1451.
[23] M. H. K. Etemad-Shahidi, A. Etemad-Shahidi, and J. Mousavi (2005), Application of fuzzy inference system in the prediction of wave parameters, Ocean Engineering, Vol.32, No.14-15, pp.1709-1725.
[24] V. R. Kashikar and S. J. Mane (2014), Wave height forecasting using artificial neural network and fuzzy logic, Int. J. Soft Comput. Artif. Intell., Vol.2, No.1, pp.45-50.
[25] L. Bozorgzadeh, M. Bakhtiari, N. S. K. Zadeh, and M. Esmaeeldoust (2019), Forecasting of Wind-Wave Height by Using Adaptive Neuro-Fuzzy Inference System and Decision Tree, J. Soft Comput. Civ. Eng., Vol.3, No.3, pp.22-36.
[26] R. P. A. C. Savenije (1995), Probabilistic admittance policy deep draught vessels, Tech. Rep., Ministry of Transport, Public Works and Water Management, Rotterdam, Netherlands.
[27] J. K. Vrijling (2004), Audit HARAP, Tech. Rep., Delft University of Technology, Delft, Netherlands.
[28] PIANC (2014), Approach channels - a guide for design, Tech. Rep., Brussels, Belgium.
[29] USACE (2006), Hydraulic design of deep-draft navigation projects, U.S. Army Corps of Engineers, Washington, DC.
[30] N. M. Quy (2008), Approach channels: risk- and simulation-based design, Ph.D. dissertation, Delft University of Technology, Delft, Netherlands.
[31] K. Ohtsu, Y. Yoshimura, M. Hirano, H. Takahashi, and M. Tsugane (2007), Design standard for fairway in next generation, Tech. Rep., National Institute for Land and Infrastructure Management, Japan.
[32] OCDI (2020), Technical Standards and Commentaries for Port and Harbour Facilities in Japan, National Institute for Land and Infrastructure Management, Japan.
[33] M. Zhang, P. Kujala, M. Musharraf, J. Zhang, and S. Hirdaris (2023), A machine learning method for the prediction of ship motion trajectories in real operational conditions, Ocean Engineering, Vol.283, p. 115045.
[34] Z. Jiang, Y. Ma, and W. Li (2024), A Data-Driven Method for Ship Motion Forecast, Journal of Marine Science and Engineering, Vol.12, No.2, p. 291.
[35] D. D’Agostino, A. Serani, F. Stern, and M. Diez (2022), Time-series forecasting for ships maneuvering in waves via recurrent-type neural networks, Journal of Ocean Engineering and Marine Energy, Vol.8, pp.479-487.
[36] J. M. J. Journée (2001), User manual of SEAWAY, Delft University of Technology, Delft, Netherlands.
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- Nguyen Thi Hong Hanh, Tran Quang Huy, INVESTIGATION OF SHORELINE PATTERNS USING SATELLITE-BASED DETECTION: A CASE STUDY OF MY KHE BEACH, CENTRAL VIETNAM , Tạp chí Khoa học Công nghệ Hàng hải: Số 85 (2026): Số 85 (01/2026)
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