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Dual-channel spatial–temporal difference graph neural ...
ACM Digital Library
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ACM Digital Library
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由 X Ouyang 著作2023被引用 4 次 — To address this problem, we propose a dual-channel spatial–temporal difference graph neural network (DC-STDGN) to forecast future PM 2.5 ...
Dual-channel spatial–temporal difference graph
ProQuest
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ProQuest
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Most existing PM2.5 2.5 prediction methods only focus on spatiotemporal dependencies. In addition, the PM2.5 2.5 diffusion process with domain knowledge in deep ...
Dual-channel spatial–temporal difference graph neural ...
ResearchGate
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2024年10月22日 — To address this problem, we propose a dual-channel spatial–temporal difference graph neural network (DC-STDGN) to forecast future PM\(_{2.5}\) ...
Dual-channel spatial–temporal difference graph neural ...
OUCI
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OUCI
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Dual-channel spatial–temporal difference graph neural network for PM$$_{2.5}$$ forecasting ; Journal: Neural Computing and Applications, 2022, № 10, p. 7475-7494.
Deep-learning architecture for PM 2.5 concentration ...
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SciOpen
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由 S Zhou 著作2024被引用 16 次 — Zhou, D. Guo, Dual-channel spatial–temporal difference graph neural network for PM＄＄_{2.5}＄＄forecasting, Neural Comput. Appl. 35 ( ...
Deep-learning architecture for PM2.5 concentration ...
National Institutes of Health (NIH) (.gov)
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National Institutes of Health (NIH) (.gov)
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由 S Zhou 著作2024被引用 16 次 — Ouyang et al. [29] proposed a dual-channel spatial-temporal difference graph neural network (DSTGNN) that also accounts for both spatial and ...
The architecture of the TCL and TC blocks
ResearchGate
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Dual-channel spatial–temporal difference graph neural network for PM2.5 2.5 _{2.5} forecasting. Article. Full-text available. Nov 2022. Xiaocao ...
Deep learning on spatiotemporal graphs: A systematic ...
ScienceDirect.com
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ScienceDirect.com
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由 A Zeghina 著作2024被引用 5 次 — In this systematic literature review, we have aimed to answer the most important questions regarding spatiotemporal graph deep learning architectures.
Graph Neural Network for spatiotemporal data
arXiv
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arXiv
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由 Y Li 著作2023被引用 9 次 — It takes into account the inherent spatial dependencies present in graph-based data structures and is particularly useful for capturing the ...
Domain knowledge-enhanced multi-spatial multi-temporal ...
ScienceDirect.com
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ScienceDirect.com
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由 Y Hu 著作2024 — We present a novel Multi-spatial Multi-temporal air quality forecasting method based on Graph Convolutional Networks and Gated Recurrent Units (M2G2).