Wang et al. (2026) Spatiotemporal Dynamic Hypergraph Neural Network for Large-scale Long-term Online Precipitation Forecasting

Identification

• Journal: IEEE Transactions on Geoscience and Remote Sensing
• Year: 2026
• Date: 2026-01-01
• Authors: Zhenghong Wang, Weiqi Huang, Zhan Lin, Yi Wang, Tianqiang Huang, Fan Zhang, Zhou Huang, Yu Liu
• DOI: 10.1109/tgrs.2026.3673143

Research Groups

[Not specified in the provided text.]

Short Summary

This paper introduces a Spatiotemporal Dynamic Hypergraph Neural Network (SDHGN) designed for large-scale, long-term online precipitation forecasting. [Further details on its core findings are not available in the provided text.]

Objective

• To develop and apply a Spatiotemporal Dynamic Hypergraph Neural Network (SDHGN) for large-scale, long-term online precipitation forecasting.

Study Configuration

• Spatial Scale: Large-scale (specific geographical extent not detailed in the provided text).
• Temporal Scale: Long-term online forecasting (specific duration or frequency not detailed in the provided text).

Methodology and Data

• Models used: Spatiotemporal Dynamic Hypergraph Neural Network (SDHGN).
• Data sources: [Not specified in the provided text.]

Main Results

[Not specified in the provided text.]

Contributions

The primary contribution is the introduction of the Spatiotemporal Dynamic Hypergraph Neural Network architecture for precipitation forecasting. [Further specific contributions are not detailed in the provided text.]

Funding

[Not specified in the provided text.]

Citation

@article{Wang2026Spatiotemporal,
  author = {Wang, Zhenghong and Huang, Weiqi and Lin, Zhan and Wang, Yi and Huang, Tianqiang and Zhang, Fan and Huang, Zhou and Liu, Yu},
  title = {Spatiotemporal Dynamic Hypergraph Neural Network for Large-scale Long-term Online Precipitation Forecasting},
  journal = {IEEE Transactions on Geoscience and Remote Sensing},
  year = {2026},
  doi = {10.1109/tgrs.2026.3673143},
  url = {https://doi.org/10.1109/tgrs.2026.3673143}
}