Prediction Model of Mining Subsidence Parameters Based on Fuzzy Clustering
In view of the inaccuracy of rock movement observation data and the inaccuracy of mining subsidence prediction parameters, a prediction model of mining subsidence parameters based on fuzzy clustering is proposed. Through the analysis of the main geological and mineral characteristics of mining subsi...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Journal of Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2022/7827104 |
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| _version_ | 1832548640008175616 |
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| author | Fei Cheng Jun Yang Ziwen Zhang Jingliang Yu Xuelian Wang Yongdong Wu Zhengyi Guo Hui Li Meng Xu |
| author_facet | Fei Cheng Jun Yang Ziwen Zhang Jingliang Yu Xuelian Wang Yongdong Wu Zhengyi Guo Hui Li Meng Xu |
| author_sort | Fei Cheng |
| collection | DOAJ |
| description | In view of the inaccuracy of rock movement observation data and the inaccuracy of mining subsidence prediction parameters, a prediction model of mining subsidence parameters based on fuzzy clustering is proposed. Through the analysis of the main geological and mineral characteristics of mining subsidence, the geological and mineral characteristics are simplified according to the third similar theorem. The feature equation is obtained by using the equation analysis method and dimension analysis method. The original fuzzy clustering method is improved, and the IWFCM_CCS algorithm based on competitive merger strategy is obtained. The data of rock movement observation are analyzed by fuzzy clustering. The membership matrix and clustering center of observation station data are obtained, and the regression model based on the weight of membership degree is established. The accuracy and feasibility of the parameter prediction model are verified by analyzing and comparing the actual measurement data and the predicted results of the model. The method reduces the error of the predicted parameters caused by the observation data and provides a method for the future calculation of the predicted parameters. |
| format | Article |
| id | doaj-art-d5c0f7c07f454d57901225e3d32b39c2 |
| institution | Kabale University |
| issn | 2314-4785 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Mathematics |
| spelling | doaj-art-d5c0f7c07f454d57901225e3d32b39c22025-02-03T06:13:30ZengWileyJournal of Mathematics2314-47852022-01-01202210.1155/2022/7827104Prediction Model of Mining Subsidence Parameters Based on Fuzzy ClusteringFei Cheng0Jun Yang1Ziwen Zhang2Jingliang Yu3Xuelian Wang4Yongdong Wu5Zhengyi Guo6Hui Li7Meng Xu8Shanxi Engineering Vocational CollegeChongqing Transportation Vocational CollegeGuangzhou Maritime UniversityGuangzhou Maritime UniversityGuangzhou Maritime UniversityCenter of Guangzhou Maritime Survey and MappingGuangxi Zhuang Autonomous Region 274 Geological TeamChemical Geological Survey Institute of Liaoning ProvinceBeijing Aerospace Titan Technology Co. LTDIn view of the inaccuracy of rock movement observation data and the inaccuracy of mining subsidence prediction parameters, a prediction model of mining subsidence parameters based on fuzzy clustering is proposed. Through the analysis of the main geological and mineral characteristics of mining subsidence, the geological and mineral characteristics are simplified according to the third similar theorem. The feature equation is obtained by using the equation analysis method and dimension analysis method. The original fuzzy clustering method is improved, and the IWFCM_CCS algorithm based on competitive merger strategy is obtained. The data of rock movement observation are analyzed by fuzzy clustering. The membership matrix and clustering center of observation station data are obtained, and the regression model based on the weight of membership degree is established. The accuracy and feasibility of the parameter prediction model are verified by analyzing and comparing the actual measurement data and the predicted results of the model. The method reduces the error of the predicted parameters caused by the observation data and provides a method for the future calculation of the predicted parameters.http://dx.doi.org/10.1155/2022/7827104 |
| spellingShingle | Fei Cheng Jun Yang Ziwen Zhang Jingliang Yu Xuelian Wang Yongdong Wu Zhengyi Guo Hui Li Meng Xu Prediction Model of Mining Subsidence Parameters Based on Fuzzy Clustering Journal of Mathematics |
| title | Prediction Model of Mining Subsidence Parameters Based on Fuzzy Clustering |
| title_full | Prediction Model of Mining Subsidence Parameters Based on Fuzzy Clustering |
| title_fullStr | Prediction Model of Mining Subsidence Parameters Based on Fuzzy Clustering |
| title_full_unstemmed | Prediction Model of Mining Subsidence Parameters Based on Fuzzy Clustering |
| title_short | Prediction Model of Mining Subsidence Parameters Based on Fuzzy Clustering |
| title_sort | prediction model of mining subsidence parameters based on fuzzy clustering |
| url | http://dx.doi.org/10.1155/2022/7827104 |
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