| Autor: |
Wang Kai, Li Kangnan, Du Feng, Zhang Xiang, Wang Yanhai, Zhou Jiaxu |
| Jazyk: |
English<br />Chinese |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
矿业科学学报, Vol 8, Iss 5, Pp 613-622 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2096-2193 |
| DOI: |
10.19606/j.cnki.jmst.2023.05.003 |
| Popis: |
As deep mining becomes prevalent in China's coal mining industry, coal-gas compound dynamic disasters pose increasing threat to the safety production of coal mines. This paper adopts the field data of Pingmei No. 8 coal mine for analysis, with the attempt to predict coal-gas compound dynamic disaster through convolutional neural network. Following the routine of the big data processing, we first employed Box-plot analysis and multiple interpolation method(MI)to clean the data. Combined with grey relation analysis(GRA), we established a coal-gas compound dynamic disaster index system. Then, principal component analysis(PCA)is used for dimensionality reduction of the data. Combined with the convolution neural network(CNN)in deep learning, we established the coal-gas compound dynamic disaster prediction model based on BMGP-CNN. The field data is used to compare and verify this model with BP, random forest(RF), support vector machine(SVM)and artificial neural network(ANN). It is found that BMGP-CNN model yields prediction results with satisfactory accuracy and quick convergence. The results offer implications for the prediction and prevention of coal-gas compound dynamic disasters. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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