Risk prediction of coal and gas outburst
In order to solve the problems of low accuracy and slow response speed of existing support vector machine (SVM)-based coal and gas outburst prediction methods, a risk prediction method of coal and gas outburst based on improved grey wolf optimizer (IGWO) optimized SVM is proposed. The influence degr...
| Main Authors: | , , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Industry and Mine Automation
2022-03-01
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| Series: | Gong-kuang zidonghua |
| Subjects: | |
| Online Access: | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2021070072 |
| _version_ | 1797869002484088832 |
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| author | LI Yan NAN Xinyuan LIN Wanke |
| author_facet | LI Yan NAN Xinyuan LIN Wanke |
| author_sort | LI Yan |
| collection | DOAJ |
| description | In order to solve the problems of low accuracy and slow response speed of existing support vector machine (SVM)-based coal and gas outburst prediction methods, a risk prediction method of coal and gas outburst based on improved grey wolf optimizer (IGWO) optimized SVM is proposed. The influence degree of each influencing factor on coal and gas outburst is analyzed by using the grey relational entropy weight method, and gas pressure, gas content, initial gas diffusion speed and mining depth are extracted as main control factors of coal and gas outburst according to the correlation degree order, and the main control factors are divided into a training set and a test set, and normalized. In order to improve the defects of the traditional grey wolf optimizer (GWO) population easily falling into local optimum and slow optimization speed, the out-of-bounds processing mechanism and the random difference mutation strategy embedded in Levy flight are introduced to improve the grey wolf optimizer (ie IGWO), so as to improve the convergence precision and speed of GWO effectively. The core parameters and penalty parameters of SVM are optimized by IGWO, and the main control factors of coal and gas outburst are input into IGWO-SVM for classification. And the classification results are compared with the actual test set so as to realize the risk prediction of coal and gas outburst. The simulation results show that compared with the prediction methods based on whale optimization algorithm-SVM ( WOA-SVM), grey wolf optimizer-SVM ( GWO-SVM) and particle swarm optimization-SVM ( PSO-SVM), the prediction method based on IGWO-SVM has higher prediction precision, and can meet the precision and reliability requirements of coal and gas outburst prediction while improving the operation efficiency of SVM. The accuracy rate reaches 96.67% and the prediction speed is 5.58 s. |
| first_indexed | 2024-04-10T00:04:41Z |
| format | Article |
| id | doaj.art-9be7a54782854492abd515b4b0944de6 |
| institution | Directory Open Access Journal |
| issn | 1671-251X |
| language | zho |
| last_indexed | 2024-04-10T00:04:41Z |
| publishDate | 2022-03-01 |
| publisher | Editorial Department of Industry and Mine Automation |
| record_format | Article |
| series | Gong-kuang zidonghua |
| spelling | doaj.art-9be7a54782854492abd515b4b0944de62023-03-17T01:03:00ZzhoEditorial Department of Industry and Mine AutomationGong-kuang zidonghua1671-251X2022-03-014839910610.13272/j.issn.1671-251x.2021070072Risk prediction of coal and gas outburstLI YanNAN XinyuanLIN WankeIn order to solve the problems of low accuracy and slow response speed of existing support vector machine (SVM)-based coal and gas outburst prediction methods, a risk prediction method of coal and gas outburst based on improved grey wolf optimizer (IGWO) optimized SVM is proposed. The influence degree of each influencing factor on coal and gas outburst is analyzed by using the grey relational entropy weight method, and gas pressure, gas content, initial gas diffusion speed and mining depth are extracted as main control factors of coal and gas outburst according to the correlation degree order, and the main control factors are divided into a training set and a test set, and normalized. In order to improve the defects of the traditional grey wolf optimizer (GWO) population easily falling into local optimum and slow optimization speed, the out-of-bounds processing mechanism and the random difference mutation strategy embedded in Levy flight are introduced to improve the grey wolf optimizer (ie IGWO), so as to improve the convergence precision and speed of GWO effectively. The core parameters and penalty parameters of SVM are optimized by IGWO, and the main control factors of coal and gas outburst are input into IGWO-SVM for classification. And the classification results are compared with the actual test set so as to realize the risk prediction of coal and gas outburst. The simulation results show that compared with the prediction methods based on whale optimization algorithm-SVM ( WOA-SVM), grey wolf optimizer-SVM ( GWO-SVM) and particle swarm optimization-SVM ( PSO-SVM), the prediction method based on IGWO-SVM has higher prediction precision, and can meet the precision and reliability requirements of coal and gas outburst prediction while improving the operation efficiency of SVM. The accuracy rate reaches 96.67% and the prediction speed is 5.58 s.http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2021070072coal and gas outburstrisk predictiongrey relational entropysupport vector machineout-of-bounds processing mechanismlevy flightgrey wolf algorithm |
| spellingShingle | LI Yan NAN Xinyuan LIN Wanke Risk prediction of coal and gas outburst Gong-kuang zidonghua coal and gas outburst risk prediction grey relational entropy support vector machine out-of-bounds processing mechanism levy flight grey wolf algorithm |
| title | Risk prediction of coal and gas outburst |
| title_full | Risk prediction of coal and gas outburst |
| title_fullStr | Risk prediction of coal and gas outburst |
| title_full_unstemmed | Risk prediction of coal and gas outburst |
| title_short | Risk prediction of coal and gas outburst |
| title_sort | risk prediction of coal and gas outburst |
| topic | coal and gas outburst risk prediction grey relational entropy support vector machine out-of-bounds processing mechanism levy flight grey wolf algorithm |
| url | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2021070072 |
| work_keys_str_mv | AT liyan riskpredictionofcoalandgasoutburst AT nanxinyuan riskpredictionofcoalandgasoutburst AT linwanke riskpredictionofcoalandgasoutburst |