Calculation and measurement of tide height for the navigation of ship at high tide using artificial neural network

• Autorzy: Li Q. , Yang B.-D. , Hong B.-G.
• Języki publikacji: EN

Abstrakty

EN

Accurate tide height is crucial for the safe navigation of large deep-draft ships when they enter and leave the port. We have proposed an accurate forecasting method for the tide heights from the observation data and neural networks, which can easily calculate the tidal window period of large deep-draft ships’ navigation through long channels at high tide. Moreover, an artificial neural network is established for the tide height from the observation of tide heights before their current time node. For an ideal forecast, the neural network was optimized for one year with the tide height data of Huanghua Port. In case of large ships, their tidal characteristics of channels for are complex. A new method is proposed for the observation of multiple stations and artificial neural networks of each observation station. When ships are navigating through the port, the tide height is predicted from the observed data and forecast tide heights of multiple observation stations. Thus, a valid tidal window period is secured when the ships enter the port. Comparative analysis of the ship’s tidal window period with that of the measured one can lead us to conclude that the forecasted data has a strong correlation with the measurement. So, our proposed algorithm can accurately predict the tide height and calculate the node timing when the ship enters and depart the port. Finally, these results can be applied for the safe navigation of large deep-draft ships when the port is at high tide

• Wydawca: -
• Czasopismo: Polish Maritime Research
• Rocznik: 2018
• Tom: 25
• Numer: Special Issue S3
• Opis fizyczny: p.99-110,fig.,ref.

Twórcy

autor

Li Q.

• Navigation College, Dalian Maritime University, 116026 Dalian, Liaoning, China

autor

Yang B.-D.

• Pilot Station of Huanghua Port, Huanghua, Hebei, China

autor

Hong B.-G.

• Navigation College, Dalian Maritime University, Dalian, Liaoning, China

Bibliografia

• 1. Bao-chun, L., Da-qi, Z.: Analysis of Safety Measures for the Entry of Large Deep Draught Ships Under the Same Tide.China Water Transport, 2016,9(1), Pp. 34-35.
• 2. Jin-zhong, Z., Song-shan, Y.: Analysis of Design Water Level for Huanghua Port 200 000 DWT Channel. Port Engineering Technology, 2016, 53(6), Pp.14-16.
• 3. Ning, Z., Tie-zheng, S.: Analysis of Method of Calculating the Depth of Waterway by Segmented Artificial Long Channel. China Water Transport, 2017, 17(7), Pp.190-194.
• 4. Cun-bao, T., Liang, Z., Jing-xian, L.: Water Depth Investigation for Dongjiakou Port Receiving 400 000t Carrier by Tide. Navigation of China, 2017, 40 (2), Pp. 60-64.
• 5. Zhi-yang, H., Yuan, X.: Research and application of a new method for the calculation of ridable high tide level.The Ocean Engineering, 2017, 35(2), 2017, Pp. 83-88.
• 6. Yuan, X., Zhi-yang, H., Hong-feng, G.: On riding high tide level in a long waterway at a tidal estuary. Port & Waterway Engineering, 2011, 6(5), Pp.1-6.
• 7. Zhi-yang, H., Jian-feng, Z.: Research and application of ridable tide level considering the effect of tide current. The Ocean Engineering, 2018, 36(3), Pp.104-109.
• 8. Feng, M., Hong-bo, C.: An analysis for entering a port considering tide based on high-precise instantaneous depth model.Science of Surveying and Mapping, 2012, 37(4), Pp. 40-42.
• 9. Ning, M., Yan, C., Jun, W.: Design throughput capacity simulation for multi-shallow waterway.Journal of Dalian Maritime University, 2011, 37(1), Pp. 63-67.
• 10. Qing-long, H., Zu-xu, G., Peng-fei, Z.:Research on the Under-Keel Clearance and Ship Squat for VLCC Navigating within Port Area. Marine Technology, 2013, 17(5), Pp.2-5
• 11. Yi, L., Yang, Y.: A Tidal Forecasting Algorithm Based on Genetic Neural Network. Electric Power Science and Engineering, 2015, 31(8), Pp.1-7
• 12. Ze, G., Zhi-gang, L., Yan, Z.: Tide Prediction in Tidal Power Generation by Harmonic Analysis and Artificial Neural Network. Journal of Hebei United University (Natural Science Edition), 2014, 36(1), Pp. 84-87.
• 13. Ru-yun, W., Lei, L., Fei, Z.: Neural network forecast model of storm surge elevation based on high tide and low tide. Marine Forecasts, 2014, 31(6), Pp. 23-27.
• 14 . Ming-chang, L., Shu-xiu, L., Zhao-chen, S.: Research on tide supplemental prediction using ANN methods under unusual weather. Chinese Journal of Computational Mechanics, 2008, 25(3), Pp. 368-372.
• 15. Salim, A.M., Dwarakish, G.S., Liju, K.V.: Weekly Prediction of Tides Using Neural Networks. Procedia Engineering, 2015, 116(1), Pp.678-682.
• 16. Lee, T.L.: Back-propagation neural network for long-term tidal predictions. Ocean Engineering, 2004, 31(2), Pp. 225-238.
• 17. Alessandro, F., Torres, R., Kjerfve, A.: Application of Artificial Neural Network (ANN) to improve forecasting of sea level. Ocean and Coastal Management, 2012, 55, Pp.101-110.
• 18. He, Z.G., Gu, X.A., Sun, X.Y., Liu, J., Wang, B.S.: A coupled immersed boundary method for simulating multiphase flows. Acta Electronica Malaysia, 2017, 1(1), Pp. 05-08.
• 19. Ibrahim, M.S., Kasim, S., Hassan, R., Mahdin, H., Ramli, A.A., Md Fudzee, M.F., Salamat, M.A.: Information Technology Club Management System. Acta Electronica Malaysia, 2018, 2(2), Pp. 01-05.
• 20. He, Z.G., Gu, X.N., Sun, X.Y., Liu, J., Wang, B.S.: An efficient pseudo-potential multiphase lattice Boltzmann simulation model for three-dimensional multiphase flows. Acta Mechanica Malaysia, 2017, 1(1), Pp. 08-10.
• 21. Luo, X.: Research on Anti-Overturning Perfomance Of Multi-Span Curved Girder Bridge with Small Radius. Acta Mechanica Malaysia, 2(1), Pp. 04-07.
• 22. Halim, H., Abdullah, R., Mohd Nor, M.J., Abdul Aziz, H., Abd Rahman, N.: Comparison Between Measured Traffic Noise in Klang Valley, Malaysia And Existing Prediction Models. Engineering Heritage Journal, 2017, 1(2), Pp. 10–14.
• 23. Hassan, M.A., Mohd Ismail, M.A.: Literature Review for The Development of Dikes’s Breach Channel Mechanism Caused by Erosion Processes During Overtopping Failure. Engineering Heritage Journal, 2017, 1(2), Pp. 23-30.

Identyfikatory

DOI

10.2478/pomr-2018-0118