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: | LI Yan, NAN Xinyuan, LIN Wanke |
---|---|
Format: | Article |
Language: | zho |
Published: |
Editorial Department of Industry and Mine Automation
2022-03-01
|
Series: | Gong-kuang zidonghua |
Subjects: | |
Online Access: | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2021070072 |
Similar Items
-
Research on Temperature Variation during Coal and Gas Outbursts: Implications for Outburst Prediction in Coal Mines
by: Chaolin Zhang, et al.
Published: (2020-09-01) -
A review on prediction and early warning methods of coal and gas outburst
by: Yunpei LIANG, et al.
Published: (2023-08-01) -
Levy flight-improved grey wolf optimizer algorithm-based support vector regression model for dam deformation prediction
by: Peng He, et al.
Published: (2023-01-01) -
System of coal and gas outburst prediction based on improved BP neural network
by: WANG Sheguo, et al.
Published: (2014-05-01) -
A review on coal and gas outburst prediction based on machine learning
by: Sheng XUE, et al.
Published: (2024-03-01)