Prediction model with multi-point relationship fusion via graph convolutional network: A case study on mining-induced surface subsidence
Accurate prediction of surface subsidence is of significance for analyzing the pattern of mining-induced surface subsidence, and for mining under buildings, railways, and water bodies. To address the problem that the existing prediction models ignore the correlation between subsidence points, result...
Main Authors: | Baoxing Jiang, Kun Zhang, Xiaopeng Liu, Yuxi Lu |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2023-01-01
|
Series: | PLoS ONE |
Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10431667/?tool=EBI |
Similar Items
-
Monitoring Mining Surface Subsidence with Multi-Temporal Three-Dimensional Unmanned Aerial Vehicle Point Cloud
by: Xiaoyu Liu, et al.
Published: (2023-01-01) -
FGCN: Image-Fused Point Cloud Semantic Segmentation with Fusion Graph Convolutional Network
by: Kun Zhang, et al.
Published: (2023-10-01) -
Analysis of Mining-Induced Delayed Surface Subsidence
by: Krzysztof Tajduś, et al.
Published: (2021-10-01) -
Mining subsidence engineering/
by: 424649 Kratzsch, Helmut -
Report on mining subsidence
by: 2338 Institution of Civil Engineers (Great Britain)