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

Yang, Hanchen; Li, Wengen; Hou, Siyun; Guan, Jihong; Zhou, Shuigeng

doi:10.1145/3597937

Cited by 8 publications

(1 citation statement)

References 37 publications

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“…Sun et al [38] proposed a temporal graph neural network for SST prediction in the northwestern Pacific Ocean; this used LSTM to capture the temporal features and a graph neural network to capture the spatial features. Yang et al [39] designed a hierarchical clustering generator to cluster SST patterns with similarities, using a graph convolutional neural network to learn spatial correlations among clusters and feeding these into an RNN for SST prediction.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Fusion Prediction of Sea Surface Temperatures Based on the Graph Convolutional Neural and Long Short-Term Memory Networks

Liu,

Wang,

et al. 2024

Water

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Sea surface temperature (SST) prediction plays an important role in scientific research, environmental protection, and other marine-related fields. However, most of the current prediction methods are not effective enough to utilize the spatial correlation of SSTs, which limits the improvement of SST prediction accuracy. Therefore, this paper first explores spatial correlation mining methods, including regular boundary division, convolutional sliding translation, and clustering neural networks. Then, spatial correlation mining through a graph convolutional neural network (GCN) is proposed, which solves the problem of the dependency on regular Euclidian space and the lack of spatial correlation around the boundary of groups for the above three methods. Based on that, this paper combines the spatial advantages of the GCN and the temporal advantages of the long short-term memory network (LSTM) and proposes a spatiotemporal fusion model (GCN-LSTM) for SST prediction. The proposed model can capture SST features in both the spatial and temporal dimensions more effectively and complete the SST prediction by spatiotemporal fusion. The experiments prove that the proposed model greatly improves the prediction accuracy and is an effective model for SST prediction.

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Section: Introductionmentioning

confidence: 99%

Spatiotemporal Fusion Prediction of Sea Surface Temperatures Based on the Graph Convolutional Neural and Long Short-Term Memory Networks

Liu,

Wang,

et al. 2024

Water

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Deep learning for sea surface temperature applications: A comprehensive bibliometric analysis and methodological approach

Boufeniza,

Jingjia,

Abdela

et al. 2024

Geography and Environment

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This study explored the potential application of deep learning techniques in sea surface temperature (SST) investigations using a mixed method, bibliometric analysis and methodological approach. CiteSpace software was utilized for a bibliometric study on 137 academic publications from 2018 to 2023. Various databases were employed for methodological analysis, which involved examining publications based on models, methodologies, applications and research areas. The data were manually organized in a relational framework of an SQL database. The analysis underscored China's prominence as a leader in the extensive research devoted to this field. The United States of America and the United Kingdom played pivotal roles in providing the essential data that served as the foundation for these studies. Moreover, the long short‐term memory (LSTM) algorithm was the predominant computational deep learning algorithm extensively used in this specific context. The analysis highlighted significant knowledge gaps in areas such as SST forecasting, modelling, satellite remote sensing, extreme events and data reconstruction. Future scientists need to show more interest in these and related subjects, while Chinese and American scientists should prioritize paper quality over quantity. Additionally, fostering stronger collaborations between universities and institutions is vital for further advancements. Ultimately, this study offers valuable insights into hotspot research areas and development processes, establishing the foundation for research and suggesting possible avenues for future development. The results of this evaluation serve as an essential guide for researchers and modellers involved in prediction initiatives using deep learning.

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Inductive Spatial Temporal Prediction Under Data Drift with Informative Graph Neural Network

Zheng,

Saxena,

Cao

et al. 2024

Lecture Notes in Computer Science

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

Cited by 8 publications

References 37 publications

Spatiotemporal Fusion Prediction of Sea Surface Temperatures Based on the Graph Convolutional Neural and Long Short-Term Memory Networks

Spatiotemporal Fusion Prediction of Sea Surface Temperatures Based on the Graph Convolutional Neural and Long Short-Term Memory Networks

Deep learning for sea surface temperature applications: A comprehensive bibliometric analysis and methodological approach

Inductive Spatial Temporal Prediction Under Data Drift with Informative Graph Neural Network

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