Application of BigML in the Classification Evaluation of Top Coal Caving | Zendy

Meng Wang | Zendy; Caiwang Tai | Zendy; Qiaofeng Zhang | Zendy; Zongwei Yang | Zendy; Jiazheng Li | Zendy; Kejun Shen | Zendy; Kang Wang | Zendy

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Application of BigML in the Classification Evaluation of Top Coal Caving

Author(s) -

Meng Wang,

Caiwang Tai,

Qiaofeng Zhang,

Zongwei Yang,

Jiazheng Li,

Kejun Shen,

Kang Wang

Publication year - 2021

Publication title -

shock and vibration

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.418

H-Index - 45

eISSN - 1875-9203

pISSN - 1070-9622

DOI - 10.1155/2021/8552247

Subject(s) - coal , mining engineering , coal mining , engineering , hard coal , waste management

Longwall top coal caving mining is one of the main methods of mining thick coal seams in China. Therefore, carrying out the classification evaluation of top coal caving is of great significance to ensure mining success and reduce the risk of mining technology. In order to realize the classification evaluation of top coal caving, this article introduces the method of using BigML to establish the classification evaluation model of top coal caving. Furthermore, using the data from the CNKI database as sample data, a classification evaluation model of top coal caving is established on BigML. After training, testing, and optimization, the model is used to evaluate the top coal caving in No. 3 coal seam of Gucheng Coal Mine, and the evaluation result is grade 1, which is consistent with the engineering practice. The final research results show that the application of BigML in the classification evaluation of top coal caving is successful; the evaluation of top coal caving through BigML is reliable; BigML provides another scientific reliability way for the classification evaluation of top coal caving.

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