Prediction of Coal Mining Subsidence Based on Machine Learning Probability Theory
Geological disasters such as subsidence caused by mining have been continuously affecting people’s production and life. Therefore, how to predict the occurrence of geological disasters such as mining subsidence is an urgent technical problem. The study of mining subsidence prediction can effectively...
Saved in:
Main Authors: | Xiaohong Tian, Xinyuan Jin, Xinwei He |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2022-01-01
|
Series: | Journal of Electrical and Computer Engineering |
Online Access: | http://dx.doi.org/10.1155/2022/9772539 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Retracted: Prediction of Coal Mining Subsidence Based on Machine Learning Probability Theory
by: Journal of Electrical and Computer Engineering
Published: (2022-01-01) -
Research on Key Factors Influencing Surface Subsidence of Paste Backfilling Mining in Thick Coal Seam of Deep Mine
by: Meng Zhang, et al.
Published: (2021-01-01) -
Theoretical Analysis of Mining Induced Overburden Subsidence Boundary with the Horizontal Coal Seam Mining
by: Weitao Yan, et al.
Published: (2021-01-01) -
Prediction Model of Mining Subsidence Parameters Based on Fuzzy Clustering
by: Fei Cheng, et al.
Published: (2022-01-01) -
Additional Stress on a Buried Pipeline under the Influence of Coal Mining Subsidence
by: Ping Xu, et al.
Published: (2018-01-01)