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: | , , , |
|---|---|
| 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 |
| _version_ | 1797736602279084032 |
|---|---|
| author | Baoxing Jiang Kun Zhang Xiaopeng Liu Yuxi Lu |
| author_facet | Baoxing Jiang Kun Zhang Xiaopeng Liu Yuxi Lu |
| author_sort | Baoxing Jiang |
| collection | DOAJ |
| description | 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, resulting in large prediction errors, a Multi-point Relationship Fusion prediction model based on Graph Convolutional Networks (MRF-GCN) for mining-induced subsidence was proposed. Taking the surface subsidence in 82/83 mining area of Yuandian No. 2 Mine in Anhui Province in eastern China as an example, the surface deformation data obtained from 250 InSAR images captured by Sentinel-1A satellite from 2018 to 2022, combined with GNSS observation data, were used for modeling. The deformation pattern of each single observation point was obtained by feeding their deformation observation data into the LSTM encoder, after that, the relationship graph was created based on the correlation between points in the observation network and MRF-GCN was established. Then the prediction results came out through a nonlinear activation function of neural network. The research shows that the R2R2 value of MRF-GCN model was 0.865 0, much larger than that of Long-Short Term Memory (LSTM) and other conventional models, while mean square error (MSE) of MRF-GCN model was 1.59 899, much smaller than that of LSTM and other conventional models. Therefore, the MRF-GCN model has better prediction accuracy than other models and can be applied to predicting surface subsidence in large areas. |
| first_indexed | 2024-03-12T13:16:16Z |
| format | Article |
| id | doaj.art-2c08825c607f4331925d551b6af96428 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-03-12T13:16:16Z |
| publishDate | 2023-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-2c08825c607f4331925d551b6af964282023-08-27T05:31:52ZengPublic Library of Science (PLoS)PLoS ONE1932-62032023-01-01188Prediction model with multi-point relationship fusion via graph convolutional network: A case study on mining-induced surface subsidenceBaoxing JiangKun ZhangXiaopeng LiuYuxi LuAccurate 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, resulting in large prediction errors, a Multi-point Relationship Fusion prediction model based on Graph Convolutional Networks (MRF-GCN) for mining-induced subsidence was proposed. Taking the surface subsidence in 82/83 mining area of Yuandian No. 2 Mine in Anhui Province in eastern China as an example, the surface deformation data obtained from 250 InSAR images captured by Sentinel-1A satellite from 2018 to 2022, combined with GNSS observation data, were used for modeling. The deformation pattern of each single observation point was obtained by feeding their deformation observation data into the LSTM encoder, after that, the relationship graph was created based on the correlation between points in the observation network and MRF-GCN was established. Then the prediction results came out through a nonlinear activation function of neural network. The research shows that the R2R2 value of MRF-GCN model was 0.865 0, much larger than that of Long-Short Term Memory (LSTM) and other conventional models, while mean square error (MSE) of MRF-GCN model was 1.59 899, much smaller than that of LSTM and other conventional models. Therefore, the MRF-GCN model has better prediction accuracy than other models and can be applied to predicting surface subsidence in large areas.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10431667/?tool=EBI |
| spellingShingle | Baoxing Jiang Kun Zhang Xiaopeng Liu Yuxi Lu Prediction model with multi-point relationship fusion via graph convolutional network: A case study on mining-induced surface subsidence PLoS ONE |
| title | Prediction model with multi-point relationship fusion via graph convolutional network: A case study on mining-induced surface subsidence |
| title_full | Prediction model with multi-point relationship fusion via graph convolutional network: A case study on mining-induced surface subsidence |
| title_fullStr | Prediction model with multi-point relationship fusion via graph convolutional network: A case study on mining-induced surface subsidence |
| title_full_unstemmed | Prediction model with multi-point relationship fusion via graph convolutional network: A case study on mining-induced surface subsidence |
| title_short | Prediction model with multi-point relationship fusion via graph convolutional network: A case study on mining-induced surface subsidence |
| title_sort | prediction model with multi point relationship fusion via graph convolutional network a case study on mining induced surface subsidence |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10431667/?tool=EBI |
| work_keys_str_mv | AT baoxingjiang predictionmodelwithmultipointrelationshipfusionviagraphconvolutionalnetworkacasestudyonmininginducedsurfacesubsidence AT kunzhang predictionmodelwithmultipointrelationshipfusionviagraphconvolutionalnetworkacasestudyonmininginducedsurfacesubsidence AT xiaopengliu predictionmodelwithmultipointrelationshipfusionviagraphconvolutionalnetworkacasestudyonmininginducedsurfacesubsidence AT yuxilu predictionmodelwithmultipointrelationshipfusionviagraphconvolutionalnetworkacasestudyonmininginducedsurfacesubsidence |