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...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/9772539 |
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| author | Xiaohong Tian Xinyuan Jin Xinwei He |
| author_facet | Xiaohong Tian Xinyuan Jin Xinwei He |
| author_sort | Xiaohong Tian |
| collection | DOAJ |
| description | 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 guide the safe production of mining area, and it is of great practical significance. Probability theory is a discipline used to express uncertainty. It provides methods and axioms to quantify and deduce uncertainty, which allows us to reason when uncertainty exists. This paper mainly studies the prediction process of mining subsidence based on machine learning probability theory. In this paper, the mining subsidence problem is studied. The main research contents are as follows: through the research and analysis of the mining subsidence prediction method, the probability integral method is determined as the theoretical basis for the study. The mining parameters are obtained from the three-dimensional geological body model of the mining area, and the prediction parameters are calculated by the least square fitting. This paper makes a detailed plan for the overall design of mining subsidence prediction module, adopts the form of independent research and development and joint development with the existing software, designs five submodules according to the design objectives, principles, and functional needs of the module, and studies the application of mining subsidence prediction module. In this study, it is found that the predicted data of the maximum settlement value appear at the 23rd point, and the maximum value is 1567 mm, which is completely consistent with the change trend of the measured value. The settlement value is calculated from the edge position of the working face to the position where the chariot gradually increases, which conforms to the change law of surface movement and settlement. |
| format | Article |
| id | doaj-art-d5a1d4e40bec4d8b8744db0800f02a0f |
| institution | Kabale University |
| issn | 2090-0155 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-d5a1d4e40bec4d8b8744db0800f02a0f2025-02-03T06:05:25ZengWileyJournal of Electrical and Computer Engineering2090-01552022-01-01202210.1155/2022/9772539Prediction of Coal Mining Subsidence Based on Machine Learning Probability TheoryXiaohong Tian0Xinyuan Jin1Xinwei He2Coal and Chemical Industry CollegeWenzhou University of TechnologyResearch and Production DepartmentGeological 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 guide the safe production of mining area, and it is of great practical significance. Probability theory is a discipline used to express uncertainty. It provides methods and axioms to quantify and deduce uncertainty, which allows us to reason when uncertainty exists. This paper mainly studies the prediction process of mining subsidence based on machine learning probability theory. In this paper, the mining subsidence problem is studied. The main research contents are as follows: through the research and analysis of the mining subsidence prediction method, the probability integral method is determined as the theoretical basis for the study. The mining parameters are obtained from the three-dimensional geological body model of the mining area, and the prediction parameters are calculated by the least square fitting. This paper makes a detailed plan for the overall design of mining subsidence prediction module, adopts the form of independent research and development and joint development with the existing software, designs five submodules according to the design objectives, principles, and functional needs of the module, and studies the application of mining subsidence prediction module. In this study, it is found that the predicted data of the maximum settlement value appear at the 23rd point, and the maximum value is 1567 mm, which is completely consistent with the change trend of the measured value. The settlement value is calculated from the edge position of the working face to the position where the chariot gradually increases, which conforms to the change law of surface movement and settlement.http://dx.doi.org/10.1155/2022/9772539 |
| spellingShingle | Xiaohong Tian Xinyuan Jin Xinwei He Prediction of Coal Mining Subsidence Based on Machine Learning Probability Theory Journal of Electrical and Computer Engineering |
| title | Prediction of Coal Mining Subsidence Based on Machine Learning Probability Theory |
| title_full | Prediction of Coal Mining Subsidence Based on Machine Learning Probability Theory |
| title_fullStr | Prediction of Coal Mining Subsidence Based on Machine Learning Probability Theory |
| title_full_unstemmed | Prediction of Coal Mining Subsidence Based on Machine Learning Probability Theory |
| title_short | Prediction of Coal Mining Subsidence Based on Machine Learning Probability Theory |
| title_sort | prediction of coal mining subsidence based on machine learning probability theory |
| url | http://dx.doi.org/10.1155/2022/9772539 |
| work_keys_str_mv | AT xiaohongtian predictionofcoalminingsubsidencebasedonmachinelearningprobabilitytheory AT xinyuanjin predictionofcoalminingsubsidencebasedonmachinelearningprobabilitytheory AT xinweihe predictionofcoalminingsubsidencebasedonmachinelearningprobabilitytheory |