A forecasting model for wave heights based on a long short-term memory neural network

Song Gao; Juan Huang; Yaru Li; Guiyan Liu; Fan Bi; Zhipeng Bai

doi:10.1007/s13131-020-1680-3

Volume 40 Issue 1

Feb. 2021

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Article Navigation > Acta Oceanologica Sinica > 2021 > 40(1): 62-69

WANG Zhenfeng, SUN Zhipeng, ZHANG Daojun, ZHU Jitian, LI Xushen, HUANG Baojia, GUO Minggang, JIANG Rufeng. Geology and hydrocarbon accumulations in the deepwater of the northwestern South China Sea—with focus on natural gas[J]. Acta Oceanologica Sinica, 2015, 34(10): 57-70. doi: 10.1007/s13131-015-0715-7

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Song Gao, Juan Huang, Yaru Li, Guiyan Liu, Fan Bi, Zhipeng Bai. A forecasting model for wave heights based on a long short-term memory neural network[J]. Acta Oceanologica Sinica, 2021, 40(1): 62-69. doi: 10.1007/s13131-020-1680-3

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A forecasting model for wave heights based on a long short-term memory neural network

doi: 10.1007/s13131-020-1680-3

Song Gao^{1, 2},
Juan Huang^{1, 2},
Yaru Li^{1, 2
,
,},
Guiyan Liu^{1, 2},
Fan Bi^{1, 2},
Zhipeng Bai³

1.
North China Sea Marine Forecasting Center of State Oceanic Administration, Qingdao 266061, China
2.
Shandong Provincial Key Laboratory of Marine Ecological Environment and Disaster Prevention and Mitigation, Qingdao 266061, China
3.
Mailbox 5111, Beijing 100094, China

Funds: The National Key R&D Program of China under contract No. 2016YFC1402103.

More Information

Corresponding author: E-mail: liyaru@ncs.mnr.gov.cn
Received Date: 2020-04-12
Accepted Date: 2020-06-02

Available Online: 2021-04-21

Publish Date: 2021-01-25

Abstract

Abstract

To explore new operational forecasting methods of waves, a forecasting model for wave heights at three stations in the Bohai Sea has been developed. This model is based on long short-term memory (LSTM) neural network with sea surface wind and wave heights as training samples. The prediction performance of the model is evaluated, and the error analysis shows that when using the same set of numerically predicted sea surface wind as input, the prediction error produced by the proposed LSTM model at Sta. N01 is 20%, 18% and 23% lower than the conventional numerical wave models in terms of the total root mean square error (RMSE), scatter index (SI) and mean absolute error (MAE), respectively. Particularly, for significant wave height in the range of 3–5 m, the prediction accuracy of the LSTM model is improved the most remarkably, with RMSE, SI and MAE all decreasing by 24%. It is also evident that the numbers of hidden neurons, the numbers of buoys used and the time length of training samples all have impact on the prediction accuracy. However, the prediction does not necessary improve with the increase of number of hidden neurons or number of buoys used. The experiment trained by data with the longest time length is found to perform the best overall compared to other experiments with a shorter time length for training. Overall, long short-term memory neural network was proved to be a very promising method for future development and applications in wave forecasting.
- long short-term memory,
- marine forecast,
- neural network,
- significant wave height

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References

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