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June 28, 2026Journal of Forestry ResearchOpen Access

Multi-graph spatial–temporal graph convolutional networks for predicting the spread of Dendroctonus valens in China

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Authors

HZHongwei ZhouYLYongzheng LiJYJun Yang

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Overview

Randomized trial analyzes Dendroctonus valens spread in China, suggesting effective risk prediction tools.

Key Points

  • This study aims to predict the spread of Dendroctonus valens and understand the factors influencing its risk in China.
  • Analyzed historical spatiotemporal patterns of D. valens in China.
  • Developed the MG-STGCN framework using multi-dimensional graph structures, spatial attention, and GRU.
  • Generated county-level risk predictions across China.
  • MG-STGCN achieved a recall of 89.57%, precision of 90.68%, and an F1-score of 90.12%.
  • High-risk areas primarily cluster in southern Northeast China and adjacent North China.
  • Emerging signals indicate a slight northeastward shift and westward penetration in pest spread patterns.

Cite This Study

Zhou et al. (2026) studied this question.

synapsesocial.com/papers/6a40b9f361bb0a67205c6002https://doi.org/10.1007/s11676-026-02097-w
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