research-article

HiGRN: A Hierarchical Graph Recurrent Network for Global Sea Surface Temperature Prediction

Authors:

Shuigeng ZhouAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology, Volume 14, Issue 4

Article No.: 73, Pages 1 - 19

https://doi.org/10.1145/3597937

Published: 21 July 2023 Publication History

Abstract

Sea surface temperature (SST) is one critical parameter of global climate change, and accurate SST prediction is important to various applications, e.g., weather forecasting, fishing directions, and disaster warnings. The global ocean system is unified and complex, and the SST patterns in different oceanic regions are highly diverse and correlated. However, existing data-driven SST prediction methods mainly consider the local patterns within a certain oceanic region, e.g., El Nino region and the Black sea. It is challenging but necessary to model the global SST correlations rather than that in a specific region to enhance the prediction accuracy of SST. In this work, we proposed a new method called Hierarchical Graph Recurrent Network (HiGRN) to address the issue. First, to learn the dynamic and diverse local SST patterns of specific locations, we design an adaptive node embedding with self-learned parameters to learn various SST patterns. Then we develop a hierarchical cluster generator to aggregate the locations with similar patterns into regional clusters and utilize a graph convolution network to learn the spatial correlations among these clusters. Finally, we introduce a multi-level attention mechanism to fuse the local patterns and regional correlations, and the output is fed into a recurrent network to achieve SST predictions. Extensive experiments on two real-world datasets show that our method largely outperforms the state-of-the-art SST prediction methods. The source code is available at https://github.com/Neoyanghc/HiGRN.

References

[1]

Silvestre Aguilar-Martinez and William W. Hsieh. 2009. Forecasts of tropical Pacific sea surface temperatures by neural networks and support vector regression. Int. J. Oceanogr. (2009), 1–13.

[2]

Lei Bai, Lina Yao, Salil S. Kanhere, Xianzhi Wang, and Quan Z. Sheng. 2019. STG2seq: Spatial-temporal graph to sequence model for multi-step passenger demand forecasting. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI’19). 1981–1987.

[3]

Lei Bai, Lina Yao, Can Li, Xianzhi Wang, and Can Wang. 2020. Adaptive graph convolutional recurrent network for traffic forecasting. In Advances in Neural Information Processing Systems (NIPS’20). 17804–17815.

[4]

Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun. 2014. Spectral networks and locally connected networks on graphs. In Proceedings of the 2nd International Conference on Learning Representations (ICLR’14). 1–14.

[5]

B. Buongiorno Nardelli, C. Tronconi, A. Pisano, and R. Santoleri. 2013. High and ultra-high resolution processing of satellite sea surface temperature data over southern European seas in the framework of MyOcean project. Remote Sens. Environ. 129 (2013), 1–16.

[6]

Wei Cao, Dong Wang, Jian Li, Hao Zhou, Lei Li, and Yitan Li. 2018. BRITS: Bidirectional recurrent imputation for time series. In Advances in Neural Information Processing Systems (NIPS’18). 1–11.

[7]

Jiajun Chen, Huarui He, Feng Wu, and Jie Wang. 2021. Topology-aware correlations between relations for inductive link prediction in knowledge graphs. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI’21). 6271–6278.

[8]

Weiqi Chen, Ling Chen, Yu Xie, Wei Cao, Yusong Gao, and Xiaojie Feng. 2020. Multi-range attentive bicomponent graph convolutional network for traffic forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI’20). 3529–3536.

[9]

Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 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 (EMNLP’14). 1724–1734.

[10]

Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. In Proceedings of the 30th International Conference on Neural Information Processing Systems (NIPS’16). 3844–3852.

Digital Library

[11]

J. Paige Eveson, Alistair J. Hobday, Jason R. Hartog, Claire M. Spillman, and Kirsten M. Rough. 2015. Seasonal forecasting of tuna habitat in the great Australian bight. Fish. Res. 170 (2015), 39–49.

[12]

Kan Guo, Yongli Hu, Yanfeng Sun, Sean Qian, Junbin Gao, and Baocai Yin. 2021. Hierarchical graph convolution networks for traffic forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI’21). 151–159.

[13]

Shengnan Guo, Youfang Lin, Ning Feng, Chao Song, and Huaiyu Wan. 2019. Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI’19). 922–929.

[14]

Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural Comput. (1997), 1735–1780.

Digital Library

[15]

Siyun Hou, Wengen Li, Tianying Liu, Shuigeng Zhou, Jihong Guan, Rufu Qin, and Zhenfeng Wang. 2021. D2CL: A dense dilated convolutional LSTM model for sea surface temperature prediction. IEEE J. Select. Top. Appl. Earth Observ. Remote Sens. (2021), 12514–12523.

[16]

Thomas N. Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv:1609.02907. Retrieved from https://arxiv.org/abs/160902907.

[17]

Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2012. Imagenet classification with deep convolutional neural networks. Communications of the ACM 60, 6 (2017), 84–90.

[18]

Akira Kuwano-Yoshida and Shoshiro Minobe. 2017. Storm-track response to SST fronts in the northwestern Pacific region in an AGCM. J. Clim. 30, 3 (Feb.2017), 1081–1102.

[19]

Guokun Lai, Wei-Cheng Chang, Yiming Yang, and Hanxiao Liu. 2018. Modeling long- and short-term temporal patterns with deep neural networks. In Proceedings of the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’18). 95–104.

Digital Library

[20]

Fuxian Li, Jie Feng, Huan Yan, Guangyin Jin, Depeng Jin, and Yong Li. 2021. Dynamic graph convolutional recurrent network for traffic prediction: Benchmark and solution. arXiv:2104.14917. Retrieved from https://arxiv.org/abs/2104.14917.

[21]

Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu. 2018. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. In Proceedings of the International Conference on Learning Representations (ICLR’18). 1–16.

[22]

Isis Didier Lins, Moacyr Araujo, Márcio das Chagas Moura, Marcus André Silva, and Enrique López Droguett. 2013. Prediction of sea surface temperature in the tropical Atlantic by support vector machines. Comput. Stat. Data Anal. (2013), 187–198.

[23]

Xi Liu, Tyler Wilson, Pang-Ning Tan, and Lifeng Luo. 2019. Hierarchical LSTM framework for long-term sea surface temperature forecasting. In Proceedings of the IEEE International Conference on Data Science and Advanced Analytics (DSAA’19). 41–50.

[24]

Mathias Niepert, Mohamed Ahmed, and Konstantin Kutzkov. 2016. Learning convolutional neural networks for graphs. In Proceedings of the 33rd International Conference on International Conference on Machine Learning (ICML’16). 2014–2023.

[25]

Syama Sundar Rangapuram, Matthias W. Seeger, Jan Gasthaus, Lorenzo Stella, Yuyang Wang, and Tim Januschowski. 2018. Deep state space models for time series forecasting. In Advances in Neural Information Processing Systems (NIPS’18). 1–10.

[26]

Carlos Alberto Repelli and Paulo Nobre. 2004. Statistical prediction of sea-surface temperature over the tropical Atlantic. Int. J. Climatol. 24, 1 (2004), 45–55.

[27]

Youngjoo Seo, Michaël Defferrard, Pierre Vandergheynst, and Xavier Bresson. 2018. Structured sequence modeling with graph convolutional recurrent networks. In 25th International Conference on Neural Information Processing (ICONIP’18), Part I (Lecture Notes in Computer Science), Long Cheng, Andrew Chi-Sing Leung, and Seiichi Ozawa (Eds.), Vol. 11301. Springer, 362–373.

Digital Library

[28]

Chao Shang, Jie Chen, and Jinbo Bi. 2021. Discrete graph structure learning for forecasting multiple time series. arXiv:2101.06861. Retrieved from https://arxiv.org/abs/2101.06861.

[29]

Xingjian Shi, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-kin Wong, and Wang-chun Woo. 2015. Convolutional LSTM network: A machine learning approach for precipitation nowcasting. In Proceedings of the 28th International Conference on Neural Information Processing Systems (NIPS’15). 802–810.

[30]

Le Song, Alex Smola, Arthur Gretton, Karsten M. Borgwardt, and Justin Bedo. 2007. Supervised feature selection via dependence estimation. In Proceedings of the 24th International Conference on Machine Learning (ICML’07). 823–830.

Digital Library

[31]

A. Storto and S. Masina. 2014. The CMCC global ocean physical reanalysis system (C-GLORS) version 3.1: configuration and basic validation. CMCC Research Paper RP0211.

[32]

Andrea Storto, Simona Masina, and Antonio Navarra. 2016. Evaluation of the CMCC eddy-permitting global ocean physical reanalysis system (C-GLORS, 1982– 2012) and its assimilation components. Quart. J. Roy. Meteorol. Soc. 142, 695 (2016), 738–758.

[33]

Yongjiao Sun, Xin Yao, Xin Bi, Xuechun Huang, Xiangguo Zhao, and Baiyou Qiao. 2021. Time-series graph network for sea surface temperature prediction. Big Data Res. (2021), 100237.

Digital Library

[34]

Laurens Van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 11 (2008).

[35]

Binghui Wang, Jinyuan Jia, and Neil Zhenqiang Gong. 2021. Semi-supervised node classification on graphs: Markov random fields vs. graph neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI’21). 10093–10101.

[36]

Senzhang Wang, Jiannong Cao, and Philip S. Yu. 2019. Deep learning for spatio-temporal data mining: A survey. arXiv:1906.04928. Retrieved from https://arxiv.org/abs/1906.04928.

[37]

Aiming Wu, William W. Hsieh, and Benyang Tang. 2006. Neural network forecasts of the tropical Pacific sea surface temperatures. Neural Netw. 19, 2 (2006), 145–154.

Digital Library

[38]

Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and Philip S. Yu. 2021. A comprehensive survey on graph neural networks. IEEE Trans. Neural Netw. Learn. Syst. 32, 1 (2021), 4–24.

[39]

Changjiang Xiao, Nengcheng Chen, Chuli Hu, Ke Wang, Zewei Xu, Yaping Cai, Lei Xu, Zeqiang Chen, and Jianya Gong. 2019. A spatiotemporal deep learning model for sea surface temperature field prediction using time-series satellite data. Environ. Model. Softw. 120 (2019), 104502.

[40]

Y. Xue and A. Leetmaa. 2000. Forecasts of tropical Pacific SST and sea level using a Markov model. Geophys. Res. Lett. (2000), 2701–2704.

[41]

Song Yang, Zhenning Li, Jin-Yi Yu, Xiaoming Hu, Wenjie Dong, and Shan He. 2018. El Ni\(\mathrm{\tilde{n}}\)o southern oscillation and its impact in the changing climate. Natl. Sci. Rev. 5, 6 (2018), 840–857.

[42]

Yuting Yang, Junyu Dong, Xin Sun, Estanislau Lima, Quanquan Mu, and Xinhua Wang. 2018. A CFCC-LSTM model for sea surface temperature prediction. IEEE Geosci. Remote Sens. Lett.2 (2018), 207–211.

[43]

Jiexia Ye, Juanjuan Zhao, Kejiang Ye, and Chengzhong Xu. 2020. How to build a graph-based deep learning architecture in traffic domain: A survey. IEEE Trans. Intell. Transport. Syst. (2020), 1–21.

[44]

Rex Ying, Jiaxuan You, Christopher Morris, Xiang Ren, William L. Hamilton, and Jure Leskovec. 2018. Hierarchical graph representation learning with differentiable pooling. In Proceedings of the 32nd International Conference on Neural Information Processing Systems (NIPS’18). 4805–4815.

Digital Library

[45]

Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2018. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. In Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI’18). 3634–3640.

[46]

Le Yu, Guanghui Wu, Leilei Sun, Bowen Du, and Weifeng Lv. 2022. Element-guided temporal graph representation learning for temporal sets prediction. In Proceedings of the ACM Web Conference 2022 (WWW’22). 1902–1913.

Digital Library

[47]

G.Peter Zhang. 2003. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50 (2003), 159–175.

[48]

Junbo Zhang, Yu Zheng, and Dekang Qi. 2017. Deep spatio-temporal residual networks for citywide crowd flows prediction. In Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI’17). 1655–1661.

[49]

Qin Zhang, Hui Wang, Junyu Dong, Guoqiang Zhong, and Xin Sun. 2017. Prediction of sea surface temperature using long short-term memory. IEEE Geosci. Remote Sens. Lett. 14, 10 (2017), 1745–1749.

[50]

Xiaoyu Zhang, Yongqing Li, Alejandro C. Frery, and Peng Ren. 2022. Sea surface temperature prediction with memory graph convolutional networks. IEEE Geosci. Remote Sens. Lett. 19 (2022), 1–5.

[51]

Zhen Zhang, Xinliang Pan, Tao Jiang, Baikai Sui, Chenxi Liu, and Weifu Sun. 2020. Monthly and quarterly sea surface temperature prediction based on gated recurrent unit neural network. J. Mar. Sci. Eng. 8, 4 (2020), 249–253.

[52]

Zezheng Zhao, Jianyin Zhou, and Huadong Du. 2022. Artificial intelligence powered forecast of oceanic mesoscale phenomena: A typhoon cold wake case occurring in northwest Pacific ocean. Fut. Gener. Comput. Syst. 129 (2022), 389–398.

Digital Library

[53]

Eric Zivot and Jiahui Wang. 2003. Vector Autoregressive Models for Multivariate Time Series. Springer, New York, NY, 369–413.

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HiGRN: A Hierarchical Graph Recurrent Network for Global Sea Surface Temperature Prediction

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 14, Issue 4

August 2023

481 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3596215

Editor:
Huan Liu
Arizona State University, USA

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Publication History

Published: 21 July 2023

Online AM: 22 May 2023

Accepted: 14 May 2023

Revised: 22 April 2023

Received: 04 December 2022

Published in TIST Volume 14, Issue 4

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National Natural Science Foundation of China
National Key R&D Program of China
Fundamental Research Funds for the Central Universities
Open Research Projects of Zhejiang Lab

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https://doi.org/10.1109/JSTARS.2025.3531122
Li SLiu MZhu JChen JGuo LLi WLi L(2025)LWDF: A lightweight diffusion model for sea surface temperature predictionStochastic Environmental Research and Risk Assessment10.1007/s00477-025-02940-2Online publication date: 26-Feb-2025
https://doi.org/10.1007/s00477-025-02940-2
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