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.

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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/10.3390/w16121725
Chen CLee CHuang SPeng W(2024)Credit Card Fraud Detection via Intelligent Sampling and Self-supervised LearningACM Transactions on Intelligent Systems and Technology10.1145/364128315:2(1-29)Online publication date: 28-Mar-2024
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Liu JChen CLee CHuang S(2024)Evolving Knowledge Graph Representation Learning with Multiple Attention Strategies for Citation Recommendation SystemACM Transactions on Intelligent Systems and Technology10.1145/363527315:2(1-26)Online publication date: 28-Mar-2024
https://dl.acm.org/doi/10.1145/3635273
Zhao XQi JYu YZhou L(2024)Deep learning for ocean temperature forecasting: a surveyIntelligent Marine Technology and Systems10.1007/s44295-024-00042-32:1Online publication date: 8-Oct-2024
https://doi.org/10.1007/s44295-024-00042-3
Minaei MHopke PKamangar M(2024)An in-depth investigation of global sea surface temperature behavior utilizing chaotic modelingEnvironmental Science and Pollution Research10.1007/s11356-024-33790-031:27(39823-39838)Online publication date: 4-Jun-2024
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Boufeniza RJingjia LAbdela KAlsafadi KAlsahli M(2024)Deep learning for sea surface temperature applications: A comprehensive bibliometric analysis and methodological approachGeo: Geography and Environment10.1002/geo2.15111:2Online publication date: 17-Sep-2024
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