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CNN-BiLSTM-Attention Model in Forecasting Wave Height over South-East China Seas

Lina Wang1,2,*, Xilin Deng1, Peng Ge1, Changming Dong2,3, Brandon J. Bethel3, Leqing Yang1, Jinyue Xia4

1 School of Artificial Intelligence (School of Future Technology), Nanjing University of Information Science and Technology Nanjing, 210044, China
2 Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519080, China
3 School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing, 210044, China
4 International Business Machines Corporation (IBM), New York, 10504, USA

* Corresponding Author: Lina Wang. Email: email

Computers, Materials & Continua 2022, 73(1), 2151-2168. https://doi.org/10.32604/cmc.2022.027415

Abstract

Though numerical wave models have been applied widely to significant wave height prediction, they consume massive computing memory and their accuracy needs to be further improved. In this paper, a two-dimensional (2D) significant wave height (SWH) prediction model is established for the South and East China Seas. The proposed model is trained by Wave Watch III (WW3) reanalysis data based on a convolutional neural network, the bi-directional long short-term memory and the attention mechanism (CNN-BiLSTM-Attention). It adopts the convolutional neural network to extract spatial features of original wave height to reduce the redundant information input into the BiLSTM network. Meanwhile, the BiLSTM model is applied to fully extract the features of the associated information of time series data. Besides, the attention mechanism is used to assign probability weight to the output information of the BiLSTM layer units, and finally, a training model is constructed. Up to 24-h prediction experiments are conducted under normal and extreme conditions, respectively. Under the normal wave condition, for 3-, 6-, 12- and 24-h forecasting, the mean values of the correlation coefficients on the test set are 0.996, 0.991, 0.980, and 0.945, respectively. The corresponding mean values of the root mean square errors are measured at 0.063 m, 0.105 m, 0.172 m, and 0.281 m, respectively. Under the typhoon-forced extreme condition, the model based on CNN-BiLSTM-Attention is trained by typhoon-induced SWH extracted from the WW3 reanalysis data. For 3-, 6-, 12- and 24-h forecasting, the mean values of correlation coefficients on the test set are respectively 0.993, 0.983, 0.958, and 0.921, and the averaged root mean square errors are 0.159 m, 0.257 m, 0.437 m, and 0.555 m, respectively. The model performs better than that trained by all the WW3 reanalysis data. The result suggests that the proposed algorithm can be applied to the 2D wave forecast with higher accuracy and efficiency.

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Cite This Article

APA Style
Wang, L., Deng, X., Ge, P., Dong, C., Bethel, B.J. et al. (2022). Cnn-bilstm-attention model in forecasting wave height over south-east china seas. Computers, Materials & Continua, 73(1), 2151-2168. https://doi.org/10.32604/cmc.2022.027415
Vancouver Style
Wang L, Deng X, Ge P, Dong C, Bethel BJ, Yang L, et al. Cnn-bilstm-attention model in forecasting wave height over south-east china seas. Comput Mater Contin. 2022;73(1):2151-2168 https://doi.org/10.32604/cmc.2022.027415
IEEE Style
L. Wang et al., "CNN-BiLSTM-Attention Model in Forecasting Wave Height over South-East China Seas," Comput. Mater. Contin., vol. 73, no. 1, pp. 2151-2168. 2022. https://doi.org/10.32604/cmc.2022.027415



cc Copyright © 2022 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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