QI Yi-quan, ZHANG Zhi-xu, LI Chi-wei, LI Yok-sheung, SHI Ping. Application of artificial neural network to numerical wave prediction[J]. Advances in Water Science, 2005, 16(1): 32-35.
Citation: QI Yi-quan, ZHANG Zhi-xu, LI Chi-wei, LI Yok-sheung, SHI Ping. Application of artificial neural network to numerical wave prediction[J]. Advances in Water Science, 2005, 16(1): 32-35.

Application of artificial neural network to numerical wave prediction

Funds: The project is supported by the National Natural Science Foundation of China(No.49976006).
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  • Received Date: November 18, 2003
  • Revised Date: March 19, 2004
  • The objective of this paper is to use an artificial neural network(ANN) model to train the output of a third generation wave model to better forecast the significant wave heights from buoy data.After training,the agreement between the wave model's output and the buoy data generally increases,but there is still significant disagreement when the wave height is at its peak.The significant wave heights bigger than 1.5m are selected to retrain,using the same ANN model,and the resulting improvement in the forecast is obvious since the root mean square error(RMS) between the ANN output and the buoy data decrease from 0.31 m to 0.29 m.The goal of this paper is to investigate the feasibility of using an ANN to improve a wave model's numerical wave prediction so as to develop a more accurate wave forecasting system.The results show that an ANN is an useful tool for this purpose.
  • [1]
    Kuligowski R J,Barros A P.Experiments in short term precipitation forecasting using artificial neural networks[J].Mon Weath Rev,1998,126:470-482.
    [2]
    Imrie C E,Durucan S,Korre A.River flow prediction using artificial neural networks:generation beyond the calibration range[J].J Hydrol,2000,233: 138-153.
    [3]
    Tsai C P,Lin C,Shen J N.Neural networks for wave forecasting among multi-stations[J].Ocean engineering,2002,29:1683-1695.
    [4]
    Deo M C,Kiran Kumar N.Interpolation of wave heights[J].Ocean engineering,2000,27:907-919.
    [5]
    文圣常,宇宙文.海浪理论和计算原理[M].北京:科学出版社,1984.220-228.
    [6]
    焦李成.神经网络系统理论[M].西安:西安电子科技大学出版社,1996.17-25.
    [7]
    Li C W.A split operator scheme for ocean wave simulation[J].International Journal for Numerical Methods in Fluids,1992,15:579-593.
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