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Application of Group Method of Data Handling Type Neural Network for Significant Wave Height Prediction

Received: 5 October 2019     Accepted: 22 October 2019     Published: 28 October 2019
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Abstract

The estimation of wave parameters is of great importance in coastal activities such as design studies for harbor, inshore and offshore structures, coastal erosion, sediment transport, and wave energy estimation. For this purpose, several models and approaches have been proposed to predict wave parameters, such as empirical, numerical-based approaches, and soft computing. In this study, the group method of data handling type neural network (GMDH-NN) was presented for significant wave height prediction in an attempt to suggest a new model with superior explanatory power and stability. The GMDH-NN results were compared with the field data and with a multilayer perceptron neural networks (MLPNN) model. The results indicate that the prediction accuracy and avoidance of over-fitting of the GMDH-NN method were superior to those of the MLPNN method. The percentage improvement in the root mean square error and the mean absolute percentage error of the GMDH-NN model over the MLPNN model were 72.92% and 81.02%, respectively. Also, according to the indices, the GMDH-NN model performs the best for predicting the Hs of all of the wave height ranges. That is, the GMDH-NN model is capable of predicting wave heights for different ranges. The results of the analysis suggest that the GMDH-NN-based modeling is effective in predicting significant wave height.

Published in American Journal of Neural Networks and Applications (Volume 5, Issue 2)
DOI 10.11648/j.ajnna.20190502.12
Page(s) 51-57
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

Significant Wave Height, Prediction, Group Method of Data Handling, Multilayer Perceptron

References
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Cite This Article
  • APA Style

    Moussa Sobh Elbisy, Faisal Abdulrahman Osra. (2019). Application of Group Method of Data Handling Type Neural Network for Significant Wave Height Prediction. American Journal of Neural Networks and Applications, 5(2), 51-57. https://doi.org/10.11648/j.ajnna.20190502.12

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    ACS Style

    Moussa Sobh Elbisy; Faisal Abdulrahman Osra. Application of Group Method of Data Handling Type Neural Network for Significant Wave Height Prediction. Am. J. Neural Netw. Appl. 2019, 5(2), 51-57. doi: 10.11648/j.ajnna.20190502.12

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    AMA Style

    Moussa Sobh Elbisy, Faisal Abdulrahman Osra. Application of Group Method of Data Handling Type Neural Network for Significant Wave Height Prediction. Am J Neural Netw Appl. 2019;5(2):51-57. doi: 10.11648/j.ajnna.20190502.12

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  • @article{10.11648/j.ajnna.20190502.12,
      author = {Moussa Sobh Elbisy and Faisal Abdulrahman Osra},
      title = {Application of Group Method of Data Handling Type Neural Network for Significant Wave Height Prediction},
      journal = {American Journal of Neural Networks and Applications},
      volume = {5},
      number = {2},
      pages = {51-57},
      doi = {10.11648/j.ajnna.20190502.12},
      url = {https://doi.org/10.11648/j.ajnna.20190502.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnna.20190502.12},
      abstract = {The estimation of wave parameters is of great importance in coastal activities such as design studies for harbor, inshore and offshore structures, coastal erosion, sediment transport, and wave energy estimation. For this purpose, several models and approaches have been proposed to predict wave parameters, such as empirical, numerical-based approaches, and soft computing. In this study, the group method of data handling type neural network (GMDH-NN) was presented for significant wave height prediction in an attempt to suggest a new model with superior explanatory power and stability. The GMDH-NN results were compared with the field data and with a multilayer perceptron neural networks (MLPNN) model. The results indicate that the prediction accuracy and avoidance of over-fitting of the GMDH-NN method were superior to those of the MLPNN method. The percentage improvement in the root mean square error and the mean absolute percentage error of the GMDH-NN model over the MLPNN model were 72.92% and 81.02%, respectively. Also, according to the indices, the GMDH-NN model performs the best for predicting the Hs of all of the wave height ranges. That is, the GMDH-NN model is capable of predicting wave heights for different ranges. The results of the analysis suggest that the GMDH-NN-based modeling is effective in predicting significant wave height.},
     year = {2019}
    }
    

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  • TY  - JOUR
    T1  - Application of Group Method of Data Handling Type Neural Network for Significant Wave Height Prediction
    AU  - Moussa Sobh Elbisy
    AU  - Faisal Abdulrahman Osra
    Y1  - 2019/10/28
    PY  - 2019
    N1  - https://doi.org/10.11648/j.ajnna.20190502.12
    DO  - 10.11648/j.ajnna.20190502.12
    T2  - American Journal of Neural Networks and Applications
    JF  - American Journal of Neural Networks and Applications
    JO  - American Journal of Neural Networks and Applications
    SP  - 51
    EP  - 57
    PB  - Science Publishing Group
    SN  - 2469-7419
    UR  - https://doi.org/10.11648/j.ajnna.20190502.12
    AB  - The estimation of wave parameters is of great importance in coastal activities such as design studies for harbor, inshore and offshore structures, coastal erosion, sediment transport, and wave energy estimation. For this purpose, several models and approaches have been proposed to predict wave parameters, such as empirical, numerical-based approaches, and soft computing. In this study, the group method of data handling type neural network (GMDH-NN) was presented for significant wave height prediction in an attempt to suggest a new model with superior explanatory power and stability. The GMDH-NN results were compared with the field data and with a multilayer perceptron neural networks (MLPNN) model. The results indicate that the prediction accuracy and avoidance of over-fitting of the GMDH-NN method were superior to those of the MLPNN method. The percentage improvement in the root mean square error and the mean absolute percentage error of the GMDH-NN model over the MLPNN model were 72.92% and 81.02%, respectively. Also, according to the indices, the GMDH-NN model performs the best for predicting the Hs of all of the wave height ranges. That is, the GMDH-NN model is capable of predicting wave heights for different ranges. The results of the analysis suggest that the GMDH-NN-based modeling is effective in predicting significant wave height.
    VL  - 5
    IS  - 2
    ER  - 

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Author Information
  • Civil Engineering Department, Umm Al Qura University, Makkah, Saudi Arabia

  • Civil Engineering Department, Umm Al Qura University, Makkah, Saudi Arabia

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