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March 19, 2026Journal of Engineering and Applied Science2 citationsOpen Access

Dynamic Prediction Model for Roof Deformation in Coal Mines Using ST-GNN

Dynamic prediction model for coal mine roof disasters based on spatiotemporal graph neural network

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Authors

YZYangqiang Zhang

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Overview

Dynamic forecasting model predicts roof deformation in coal mines, indicating improved safety outcomes.

Key Points

  • The aim is to develop an advanced model to predict roof deformation in coal mining by utilizing spatiotemporal characteristics.
  • Developed a spatiotemporal graph neural network model
  • Implemented data preprocessing techniques such as normalization and imputation
  • Utilized real-time sensor data for accurate predictions
  • Aggregated temporal data for improved consistency
  • Achieved an accuracy of 0.9874 in roof deformation prediction
  • Obtained precision of 0.9869 and recall of 0.9844
  • Reported an F1-score of 0.9856
  • Demonstrated superior performance compared to conventional prediction methods
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Cite This Study

Yangqiang Zhang (2026) studied this question.

synapsesocial.com/papers/69bb928c496e729e6297fef4https://doi.org/10.1186/s44147-026-00933-8
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