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ORIGINAL RESEARCH article

Front. Mar. Sci.
Sec. Physical Oceanography
Volume 11 - 2024 | doi: 10.3389/fmars.2024.1437043
This article is part of the Research Topic Prediction Models and Disaster Assessment of Ocean Waves, and the Coupling Effects of Ocean Waves in Various Ocean-Air Processes View all articles

GWSM4C-NS: improving the performance of GWSM4C in nearshore sea areas

Provisionally accepted
He Zhang He Zhang Quan Jin Quan Jin *Feng Hua Feng Hua Zeyu Wang Zeyu Wang
  • Shantou University, Shantou, China

The final, formatted version of the article will be published soon.

    Predicting nearshore significant wave heights (SWHs) with high accuracy is of great importance for coastal engineering activities, marine and coastal resource studies, and related operations. In recent years, the prediction of SWHs in two-dimensional fields based on deep learning has been gradually emerging. However, predictions for nearshore areas still suffer from insufficient resolution and poor accuracy. This paper develops a NS (NearShore) model based on the GWSM4C model (Global Wave Surrogate Model for Climate simulations). In the training area, the GWSM4C -NS model achieved a correlation coefficient (CC) of 0.977, with a spatial Root Mean Square Error (RMSE), annual mean spatial relative error (MAPE), and annual mean spatial absolute error (MAE) of 0.128 m, 10.7%, and 0.103 m, respectively. Compared to the GWSM4C model's predictions, the RMSE and MAE decreased by 59% and 60% respectively, demonstrating the model's effectiveness in enhancing nearshore SWH predictions. Additionally, applying this model to untrained sea areas to further validate its learning capability in wave energy propagation resulted in a CC of 0.951, with RMSE, MAPE, and MAE of 0.161m, 12.9%, and 0.137m, respectively. The RMSE and MAE were 43% and 39% lower than the GWSM4C model's interpolated predictions. The results shown above suggest that the newly proposed model can effectively improve the performance of GWSM4C in nearshore areas.

    Keywords: significant wave height, Nearshore waves, Convolutional Neural Networks, deep learning, Wave forecasting

    Received: 23 May 2024; Accepted: 04 Jul 2024.

    Copyright: © 2024 Zhang, Jin, Hua and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Quan Jin, Shantou University, Shantou, China

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