A significant wave height prediction method with ocean characteristics fusion and spatiotemporal dynamic graph modeling

Xiao Yin; Taoxing Wu; Jie Yu; Xiaoyu He; Lingyu Xu

doi:10.1007/s13131-024-2450-4

Volume 42 Issue 12

Dec. 2024

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Xiao Yin, Taoxing Wu, Jie Yu, Xiaoyu He, Lingyu Xu. A significant wave height prediction method with ocean characteristics fusion and spatiotemporal dynamic graph modeling[J]. Acta Oceanologica Sinica, 2024, 43(12): 13-33. doi: 10.1007/s13131-024-2450-4

Citation:

Xiao Yin, Taoxing Wu, Jie Yu, Xiaoyu He, Lingyu Xu. A significant wave height prediction method with ocean characteristics fusion and spatiotemporal dynamic graph modeling[J]. Acta Oceanologica Sinica, 2024, 43(12): 13-33. doi: 10.1007/s13131-024-2450-4

Citation:

Xiao Yin, Taoxing Wu, Jie Yu, Xiaoyu He, Lingyu Xu. A significant wave height prediction method with ocean characteristics fusion and spatiotemporal dynamic graph modeling[J]. Acta Oceanologica Sinica, 2024, 43(12): 13-33. doi: 10.1007/s13131-024-2450-4

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A significant wave height prediction method with ocean characteristics fusion and spatiotemporal dynamic graph modeling

doi: 10.1007/s13131-024-2450-4

Xiao Yin¹,
Taoxing Wu¹,
Jie Yu¹,
Xiaoyu He²,
Lingyu Xu^{1
,
,}

1.
Department of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
2.
School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China

Funds: The National Key R&D Program of China under contract No. 2021YFC3101604.

More Information

Corresponding author: xly@shu.edu.cn
Received Date: 2024-02-05
Accepted Date: 2024-09-08

Available Online: 2025-02-11

Publish Date: 2024-12-01

Abstract

Abstract

Accurate significant wave height (SWH) prediction is essential for the development and utilization of wave energy. Deep learning methods such as recurrent and convolutional neural networks have achieved good results in SWH forecasting. However, these methods do not adapt well to dynamic seasonal variations in wave data. In this study, we propose a novel method—the spatiotemporal dynamic graph (STDG) neural network. This method predicts the SWH of multiple nodes based on dynamic graph modeling and multi-characteristic fusion. First, considering the dynamic seasonal variations in the wave direction over time, the network models wave dynamic spatial dependencies from long- and short-term pattern perspectives. Second, to correlate multiple characteristics with SWH, the network introduces a cross-characteristic transformer to effectively fuse multiple characteristics. Finally, we conducted experiments on two datasets from the South China Sea and East China Sea to validate the proposed method and compared it with five prediction methods in the three categories. The experimental results show that the proposed method achieves the best performance at all predictive scales and has greater advantages for extreme value prediction. Furthermore, an analysis of the dynamic graph shows that the proposed method captures the seasonal variation mechanism of the waves.
- significant wave height forecasting,
- dynamic seasonal variation,
- dynamic graph neural networks

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References(62)

References

Abu-El-Haija S, Perozzi B, Kapoor A, et al. 2019. Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing. In: Proceedings of the 36th International Conference on Machine Learning. Long Beach: PMLR, 21–29

Ali M, Prasad R. 2019. Significant wave height forecasting via an extreme learning machine model integrated with improved complete ensemble empirical mode decomposition. Renewable and Sustainable Energy Reviews, 104: 281–295, doi: 10.1016/j.rser.2019.01.014

Ba J L, Kiros J R, Hinton G E. 2016. Layer normalization. USA: arXiv preprint arXiv: 1607. 06450, https://doi.org/10.48550/arXiv.1607.06450[2016-07-21/2023-09-01]

Chen Delong, Liu Fan, Zhang Zheqi, et al. 2021. Significant wave height prediction based on wavelet graph neural network. In: 2021 IEEE 4th International Conference on Big Data and Artificial Intelligence (BDAI). Qingdao: IEEE, 80−85

Cho K, Van Merriënboer B, Gulcehre C, et al. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. Doha: Association for Computational Linguistics, 1724–1734

Dando W A. 2005. Asia, climates of Siberia, Central and East Asia. In: Oliver J E, ed. Encyclopedia of World Climatology. Dordrecht: Springer, 102–114

Dauphin Y N, Fan A, Auli M, et al. 2017. Language modeling with gated convolutional networks. In: Proceedings of the 34th International Conference on Machine Learning. Sydney: JMLR. org, 933-941

De Andrés A D, Guanche R, Meneses L, et al. 2014. Factors that influence array layout on wave energy farms. Ocean Engineering, 82: 32–41, doi: 10.1016/j.oceaneng.2014.02.027

Ding Jie, Deng Fangyu, Liu Qi, et al. 2023. Regional forecasting of significant wave height and mean wave period using EOF-EEMD-SCINet hybrid model. Applied Ocean Research, 136: 103582, doi: 10.1016/j.apor.2023.103582

Duan Wenyang, Han Yang, Huang Limin, et al. 2016. A hybrid EMD-SVR model for the short-term prediction of significant wave height. Ocean Engineering, 124: 54–73, doi: 10.1016/j.oceaneng.2016.05.049

Feng Xi, Ma Gangfeng, Su S F, et al. 2020. A multi-layer perceptron approach for accelerated wave forecasting in Lake Michigan. Ocean Engineering, 211: 107526, doi: 10.1016/j.oceaneng.2020.107526

Gao Yuan, Miyata S, Akashi Y. 2022. Interpretable deep learning models for hourly solar radiation prediction based on graph neural network and attention. Applied Energy, 321: 119288, doi: 10.1016/j.apenergy.2022.119288

Gao Hong, Xiao Jie. 2021. Effects of power take-off parameters and harvester shape on wave energy extraction and output of a hydraulic conversion system. Applied Energy, 299: 117278, doi: 10.1016/j.apenergy.2021.117278

Ge Ming, Kerrigan E C. 2016. Short-term ocean wave forecasting using an autoregressive moving average model. In: 2016 UKACC 11th International Conference on Control (CONTROL). Belfast: IEEE, 1–6

Geng Xiulin, Xu Lingyu, He Xiaoyu, et al. 2021. Graph optimization neural network with spatio-temporal correlation learning for multi-node offshore wind speed forecasting. Renewable Energy, 180: 1014–1025, doi: 10.1016/j.renene.2021.08.066

Goda Y. 2010. Random seas and design of maritime structures. New Jersey: World Scientific Publishing Company, 3–10

Hashim R, Roy C, Motamedi S, et al. 2016. Selection of climatic parameters affecting wave height prediction using an enhanced Takagi-Sugeno-based fuzzy methodology. Renewable and Sustainable Energy Reviews, 60: 246–257, doi: 10.1016/j.rser.2016.01.098

Hersbach H, Bell B, Berrisford P, et al. 2023a. ERA5 hourly data on single levels from 1940 to present. United Kingdom: Copernicus Climate Change Service (C3S), Climate Data Store (CDS), doi: 10.24381/cds.adbb2d47, https://doi.org/10.24381/cds.bd0915c6 [2018-06-14/2023-09-01]

Hersbach H, Bell B, Berrisford P, et al. 2023b. ERA5 monthly averaged data on single levels from 1940 to present. United Kingdom: Copernicus Climate Change Service (C3S), Climate Data Store (CDS), doi: 10.24381/cds.f17050d7, https://doi.org/10.24381/cds.f17050d7 [2019-04-18/2023-09-01]

Hisaki Y. 2023. Swell and wind-wave height variability in the East China Sea. Ocean Dynamics, 73(8): 493–515, doi: 10.1007/s10236-023-01552-0

Hochreiter S, Schmidhuber J. 1997. Long short-term memory. Neural Computation, 9(8): 1735–1780, doi: 10.1162/neco.1997.9.8.1735

Khosravi A, Machado L, Nunes R O. 2018. Time-series prediction of wind speed using machine learning algorithms: a case study Osorio wind farm, Brazil. Applied Energy, 224: 550–566, doi: 10.1016/j.apenergy.2018.05.043

Kingma D P, Ba J. 2015. Adam: a method for stochastic optimization. In: 3rd International Conference on Learning Representations. San Diego: ICLR

Kitagawa G, Gersch W. 1984. A smoothness priors-state space modeling of time series with trend and seasonality. Journal of the American Statistical Association, 79(386): 378–389

Li Xinfang, Cao Jinfeng, Guo Jihong, et al. 2022. Multi-step forecasting of ocean wave height using gate recurrent unit networks with multivariate time series. Ocean Engineering, 248: 110689, doi: 10.1016/j.oceaneng.2022.110689

Li Shuang, Hao Peng, Yu Chengcheng, et al. 2021. CLTS-net: a more accurate and universal method for the long-term prediction of significant wave height. Journal of Marine Science and Engineering, 9(12): 1464, doi: 10.3390/jmse9121464

Luo Qinrui, Xu Hang, Bai Longhu. 2022. Prediction of significant wave height in hurricane area of the Atlantic Ocean using the Bi-LSTM with attention model. Ocean Engineering, 266: 112747, doi: 10.1016/j.oceaneng.2022.112747

Ma Qijie, Wang Peijun, Fan Jianhua, et al. 2022. Underground solar energy storage via energy piles: an experimental study. Applied Energy, 306: 118042, doi: 10.1016/j.apenergy.2021.118042

Mahjoobi J, Etemad-Shahidi A. 2008. An alternative approach for the prediction of significant wave heights based on classification and regression trees. Applied Ocean Research, 30(3): 172–177, doi: 10.1016/j.apor.2008.11.001

Mahjoobi J, Mosabbeb E A. 2009. Prediction of significant wave height using regressive support vector machines. Ocean Engineering, 36(5): 339–347, doi: 10.1016/j.oceaneng.2009.01.001

Mandal S, Prabaharan N. 2006. Ocean wave forecasting using recurrent neural networks. Ocean Engineering, 33(10): 1401–1410, doi: 10.1016/j.oceaneng.2005.08.007

Mirzaei A, Tangang F, Juneng L. 2015. Wave energy potential assessment in the central and southern regions of the South China Sea. Renewable Energy, 80: 454–470, doi: 10.1016/j.renene.2015.02.005

Niu Dongxiao, Yu Min, Sun Lijie, et al. 2022. Short-term multi-energy load forecasting for integrated energy systems based on CNN-BiGRU optimized by attention mechanism. Applied Energy, 313: 118801, doi: 10.1016/j.apenergy.2022.118801

Oreshkin B N, Carpov D, Chapados N, et al. 2020. N-BEATS: neural basis expansion analysis for interpretable time series forecasting. In: 8th International Conference on Learning Representations. Addis Ababa: ICLR

Owens E H. 1982. Sea conditions. In: Schwartz M, ed. Beaches and Coastal Geology. New York: Springer, 722

Pirhooshyaran M, Snyder L V. 2020. Forecasting, hindcasting and feature selection of ocean waves via recurrent and sequence-to-sequence networks. Ocean Engineering, 207: 107424, doi: 10.1016/j.oceaneng.2020.107424

Quach B, Glaser Y, Stopa J E, et al. 2021. Deep learning for predicting significant wave height from synthetic aperture radar. IEEE Transactions on Geoscience and Remote Sensing, 59(3): 1859–1867, doi: 10.1109/TGRS.2020.3003839

Reikard G, Pinson P, Bidlot J R. 2011. Forecasting ocean wave energy: the ECMWF wave model and time series methods. Ocean Engineering, 38(10): 1089–1099, doi: 10.1016/j.oceaneng.2011.04.009

Ren Yuting, Li Zhuolin, Xu Lingyu, et al. 2023. The data-based adaptive graph learning network for analysis and prediction of offshore wind speed. Energy, 267: 126590, doi: 10.1016/j.energy.2022.126590

Soares C G, Cunha C. 2000. Bivariate autoregressive models for the time series of significant wave height and mean period. Coastal Engineering, 40(4): 297–311, doi: 10.1016/S0378-3839(00)00015-6

Song Wei, Li Qichao, He Qi, et al. 2021. Determining wave height from nearshore videos based on multi-level spatiotemporal feature fusion. In: 2021 International Joint Conference on Neural Networks (IJCNN). Shenzhen: IEEE, 1–8

Ti Zilong, Song Yubing, Deng Xiaowei. 2022. Spatial-temporal wave height forecast using deep learning and public reanalysis dataset. Applied Energy, 326: 120027, doi: 10.1016/j.apenergy.2022.120027

Tolman H L. 2009. User manual and system documentation of WAVEWATCH III ^TM version 3.14. Technical note, MMAB Contribution, 276: 220.

Tsai Y H H, Bai Shaojie, Liang P P, et al. 2019. Multimodal transformer for unaligned multimodal language sequences. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Florence: Association for Computational Linguistics, 6558–6569

Tsai C P, Lin Chang, Shen Jia-N. 2002. Neural network for wave forecasting among multi-stations. Ocean Engineering, 29(13): 1683–1695, doi: 10.1016/S0029-8018(01)00112-3

Tuttle J F, Blackburn L D, Andersson K, et al. 2021. A systematic comparison of machine learning methods for modeling of dynamic processes applied to combustion emission rate modeling. Applied Energy, 292: 116886, doi: 10.1016/j.apenergy.2021.116886

Umair M, Hashmani M A, Hasan M H B. 2019. Survey of sea wave parameters classification and prediction using machine learning models. In: 2019 1st International Conference on Artificial Intelligence and Data Sciences (AiDAS). Ipoh: IEEE, 1–6

Vaswani A, Shazeer N, Parmar N, et al. 2017. Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach: Curran Associates Inc., 6000-6010

Wang Jin, Dong Changming, He Yijun. 2016. Wave climatological analysis in the East China Sea. Continental Shelf Research, 120: 26–40, doi: 10.1016/j.csr.2016.03.010

Wang Jichao, Liu Jincan, Wang Yue, et al. 2021. Spatiotemporal variations and extreme value analysis of significant wave height in the South China Sea based on 71-year long ERA5 wave reanalysis. Applied Ocean Research, 113: 102750, doi: 10.1016/j.apor.2021.102750

Wang Jichao, Sun Peidong, Liao Zhihong, et al. 2022. Long-term trend analysis of wave characteristics in the Bohai Sea based on interpolated ERA5 wave reanalysis from 1950 to 2020. Acta Oceanologica Sinica, 41(7): 97–112, doi: 10.1007/s13131-021-1974-0

Wang Wenxu, Tang Ruichun, Li Cheng, et al. 2018. A BP neural network model optimized by mind evolutionary algorithm for predicting the ocean wave heights. Ocean Engineering, 162: 98–107, doi: 10.1016/j.oceaneng.2018.04.039

Wang Jichao, Wang Yue. 2022. Evaluation of the ERA5 significant wave height against NDBC buoy data from 1979 to 2019. Marine Geodesy, 45(2): 151–165, doi: 10.1080/01490419.2021.2011502

Wu Zonghan, Pan Shirui, Long Guodong, et al. 2020. Connecting the dots: Multivariate time series forecasting with graph neural networks. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. Association for Computing Machinery, 753–763

Xiang Ling, Yang Xin, Hu Aijun, et al. 2022. Condition monitoring and anomaly detection of wind turbine based on cascaded and bidirectional deep learning networks. Applied Energy, 305: 117925, doi: 10.1016/j.apenergy.2021.117925

Xu Xinxin, Robertson B, Buckham B. 2020. A techno-economic approach to wave energy resource assessment and development site identification. Applied Energy, 260: 114317, doi: 10.1016/j.apenergy.2019.114317

Yang Bo, Wu Shaocong, Zhang Hao, et al. 2022. Wave energy converter array layout optimization: a critical and comprehensive overview. Renewable and Sustainable Energy Reviews, 167: 112668, doi: 10.1016/j.rser.2022.112668

Yu F, Koltun V. 2016. Multi-scale context aggregation by dilated convolutions. In: 4th International Conference on Learning Representations. San Juan: ICLR

Zhai Fangguo, Wu Wenfan, Gu Yanzhen, et al. 2021. Dynamics of the seasonal wave height variability in the South China Sea. International Journal of Climatology, 41(2): 934–951, doi: 10.1002/joc.6707

Zhang Chu, Hua Lei, Ji Chunlei, et al. 2022. An evolutionary robust solar radiation prediction model based on WT-CEEMDAN and IASO-optimized outlier robust extreme learning machine. Applied Energy, 322: 119518, doi: 10.1016/j.apenergy.2022.119518

Zhang Yao, Xu Lingyu, Yu Jie. 2023. Significant wave height prediction based on dynamic graph neural network with fusion of ocean characteristics. Dynamics of Atmospheres and Oceans, 103: 101388, doi: 10.1016/j.dynatmoce.2023.101388

Zhou Shuyi, Xie Wenhong, Lu Yuxiang, et al. 2021. ConvLSTM-based wave forecasts in the South and East China seas. Frontiers in Marine Science, 8: 680079, doi: 10.3389/fmars.2021.680079

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