A multivariate wave forecasting model for the Zhoushan archipelago using Long Short-Term Memory deep neural networks

Zhou Sangjun; Wei Xiaoran; Xie Xinzhe; Zhi Honghuan; Zhou Yifan; Zhu Zhengtao; Li Peiliang; Bai Yefei

doi:10.12284/hyxb2024049

Volume 46 Issue 6

Jun. 2024

Turn off MathJax

Article Contents

Article Navigation > Haiyang Xuebao > 2024 > 46(6): 14-25

Zhou Sangjun，Wei Xiaoran，Xie Xinzhe, et al. A multivariate wave forecasting model for the Zhoushan archipelago using Long Short-Term Memory deep neural networks[J]. Haiyang Xuebao，2024, 46(6)：14–25 doi: 10.12284/hyxb2024049

Citation:

PDF( 5280 KB)

A multivariate wave forecasting model for the Zhoushan archipelago using Long Short-Term Memory deep neural networks

doi: 10.12284/hyxb2024049

Zhou Sangjun^{1, 2
,},
Wei Xiaoran¹,
Xie Xinzhe^{1, 2},
Zhi Honghuan¹,
Zhou Yifan¹,
Zhu Zhengtao³,
Li Peiliang^{1, 2},
Bai Yefei^{1, 2
,
,}

1.
Ocean College, Zhejiang University, Zhoushan 316021, China
2.
Hainan Institute of Zhejiang University, Zhejiang University, Sanya 572025, China
3.
Institute of Space Technology, China Aerodynamics Research and Development Center, Mianyang 621000, China

Received Date: 2024-02-22
Rev Recd Date: 2024-05-10

Available Online: 2024-07-16

Publish Date: 2024-06-01

Abstract

Abstract

This study is based on the meteorological, oceanic, terrain and other physical quantity data covered by the observation points in the southern Zhoushan Islands from January 1, 2019 to December 31, 2021, and uses long short-term memory neural network (LSTM) to build deep learning wave forecast model. We explore the impact of the input-output sequence ratio and the number of input elements on the prediction performance of the model, realize the short-term forecast of the three elements of waves in the Zhoushan sea area, that is the significant wave height, the significant wave period and the propagation direction, and use the data during the 2022 typhoons “Hinnamnor” and “Muifa” to test the model’s prediction ability for extreme sea conditions. The research results show that the multi-element deep learning wave forecast model trained based on measured data has good prediction accuracy and stability, and can realize the prediction of extreme sea conditions. When the input-output sequence ratio is 1∶1, the model accuracy is higher. In non-extreme sea conditions, the three-element model with a prediction time of 1 hour accurately predicts significant wave height, significant wave period and direction, with Root Mean Squared Errors (RMSE) of 0.116 m, 0.569 s, and 24.583° respectively. In extreme sea conditions, the prediction RMSE for the significant wave height is 0.191 m. The increase in the number of input elements can further improve the model accuracy but also increase the training cost when the prediction time is long.
- deep learning,
- Long Short-Term Memory model,
- wave forecasting,
- Zhoushan

FullText(HTML)

References(23)

References

[1]	Qin Yue, Su Changyu, Chu Dongdong, et al. A review of application of machine learning in storm surge problems[J]. Journal of Marine Science and Engineering, 2023, 11(9): 1729. doi: 10.3390/jmse11091729
[2]	Mahjoobi J, Mosabbeb E A. Prediction of significant wave height using regressive support vector machines[J]. Ocean Engineering, 2009, 36(5): 339−347. doi: 10.1016/j.oceaneng.2009.01.001
[3]	王燕, 钟建, 张志远. 支持向量回归的机器学习方法在海浪预测中的应用[J]. 海洋预报, 2020, 37(3): 29−34. Wang Yan, Zhong Jian, Zhang Zhiyuan. Application of support vector regression in significant wave height forecasting[J]. Marine Forecasts, 2020, 37(3): 29−34.
[4]	Mahjoobi J, Etemad-Shahidi A. An alternative approach for the prediction of significant wave heights based on classification and regression trees[J]. Applied Ocean Research, 2008, 30(3): 172−177. doi: 10.1016/j.apor.2008.11.001
[5]	Ellenson A, Pei Yuanli, Wilson G, et al. An application of a machine learning algorithm to determine and describe error patterns within wave model output[J]. Coastal Engineering, 2020, 157: 103595. doi: 10.1016/j.coastaleng.2019.103595
[6]	Hu R, Fang F, Pain C C, et al. Rapid spatio-temporal flood prediction and uncertainty quantification using a deep learning method[J]. Journal of Hydrology, 2019, 575: 911−920. doi: 10.1016/j.jhydrol.2019.05.087
[7]	石绥祥, 王蕾, 余璇, 等. 长短期记忆神经网络在叶绿素a浓度预测中的应用[J]. 海洋学报, 2020, 42(2): 134−142. Shi Suixiang, Wang Lei, Yu Xuan, et al. Application of long term and short term memory neural network in prediction of chlorophyll a concentration[J]. Haiyang Xuebao, 2020, 42(2): 134−142.
[8]	Jain P, Deo M C. Real-time wave forecasts off the western Indian coast[J]. Applied Ocean Research, 2007, 29(1/2): 72−79.
[9]	Gu Chengcheng, Li Hua. Review on deep learning research and applications in wind and wave energy[J]. Energies, 2022, 15(4): 1510. doi: 10.3390/en15041510
[10]	Deo M C, Jha A, Chaphekar A S, et al. Neural networks for wave forecasting[J]. Ocean Engineering, 2001, 28(7): 889−898. doi: 10.1016/S0029-8018(00)00027-5
[11]	Peres D J, Iuppa C, Cavallaro L, et al. Significant wave height record extension by neural networks and reanalysis wind data[J]. Ocean Modelling, 2015, 94: 128−140. doi: 10.1016/j.ocemod.2015.08.002
[12]	James S C, Zhang Yushan, O’Donncha F. A machine learning framework to forecast wave conditions[J]. Coastal Engineering, 2018, 137: 1−10. doi: 10.1016/j.coastaleng.2018.03.004
[13]	Law Y Z, Santo H, Lim K Y, et al. Deterministic wave prediction for unidirectional sea-states in real-time using artificial neural network[J]. Ocean Engineering, 2020, 195: 106722. doi: 10.1016/j.oceaneng.2019.106722
[14]	Hochreiter S, Schmidhuber J. Long short-term memory[J]. Neural Computation, 1997, 9(8): 1735−1780. doi: 10.1162/neco.1997.9.8.1735
[15]	赵勇, 苏丹, 邹丽, 等. 基于LSTM神经网络的畸形波预测[J]. 华中科技大学学报(自然科学版), 2020, 48(7): 47−51. Zhao Yong, Su Dan, Zou Li, et al. Rogue wave prediction based on LSTM neural network[J]. Journal of Huazhong University of Science and Technology (Natural Science Edition), 2020, 48(7): 47−51.
[16]	高丽斌. 基于深度学习的台湾海峡及周边海域波浪预报研究[D]. 福州: 福建农林大学, 2019. Gao Libin. Wave forecasting of Taiwan Strait and its surrounding waters based on deep learning[D]. Fuzhou: Fujian Agriculture and Forestry University, 2019.
[17]	Fan Shuntao, Xiao Nianhao, Dong Sheng. A novel model to predict significant wave height based on long short-term memory network[J]. Ocean Engineering, 2020, 205: 107298. doi: 10.1016/j.oceaneng.2020.107298
[18]	Minuzzi F C, Farina L. A deep learning approach to predict significant wave height using long short-term memory[J]. Ocean Modelling, 2023, 181: 102151. doi: 10.1016/j.ocemod.2022.102151
[19]	王军, 王国欣. 宁波舟山港沿海航道保护范围研究与探讨[J]. 中国水运, 2020(4): 51−53. Wang Jun, Wang Guoxin. Research and discussion on the protection scope of coastal navigation channels in Ningbo Zhoushan Port[J]. China Water Transport, 2020(4): 51−53.
[20]	Xu Honghui, Deng Yong. Dependent evidence combination based on shearman coefficient and pearson coefficient[J]. IEEE Access, 2018, 6: 11634−11640. doi: 10.1109/ACCESS.2017.2783320
[21]	Jebli I, Belouadha FZ, Kabbaj M I, et al. Prediction of solar energy guided by pearson correlation using machine learning[J]. Energy, 2021, 224: 120109. doi: 10.1016/j.energy.2021.120109
[22]	Abadi M, Barham P, Chen Jianmin, et al. TensorFlow: a system for large-scale machine learning[C]//Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation. Savannah: USENIX Association, 2016: 265−283.
[23]	Ketkar N. Introduction to keras[M]//Ketkar N. Deep Learning with Python: A Hands-on Introduction. Berkeley: Apress, 2017: 97−111.