Affiliations: [a] School of Mechanical Electronic & Information Engineering, China University of Mining and Technology (Beijing), Beijing, China
| [b] Department of Computer Engineering, Shijiazhuang University, Shijiazhuang, China
| [c] School of Foreign Languages, Liaocheng University, Liaocheng, China
Correspondence:
[*]
Corresponding authors. Liu Xuning and Li Zhixiang, E-mails: [email protected] (Liu Xuning) and [email protected] (Li Zhixiang).
Abstract: The influencing factors of coal and gas outburst are complex, and now the accuracy and efficiency of outburst prediction are not high. In order to obtain the effective features from influencing factors and realize the accurate and fast dynamic prediction of coal and gas outburst, this article proposes an outburst prediction model based on the coupling of feature selection and intelligent optimization classifier. Firstly, in view of the redundancy and irrelevance of the influencing factors of coal and gas outburst, we use Boruta feature selection method to obtain the optimal feature subset from influencing factors of coal and gas outburst. Secondly, based on Apriori association rules mining method, the internal association relationship between coal and gas outburst influencing factors is mined, and the strong association rules existing in the influencing factors and samples that affect the classification of coal and gas outburst are extracted. Finally, svm is used to classify coal and gas outburst based on the above obtained optimal feature subset and sample data, and Bayesian optimization algorithm is used to optimize the kernel parameters of svm, and the coal and gas outburst pattern recognition prediction model is established, which is compared with the existing coal and gas outburst prediction model in literatures. Compared with the method of feature selection and association rules mining alone, the proposed model achieves the highest prediction accuracy of 93% when the feature dimension is 3, which is higher than that of Apriori association rules and Boruta feature selection, and the classification accuracy is significantly improved. However, the feature dimension decreased significantly, the results show that the proposed model is better than other prediction models, which further verifies the accuracy and applicability of the coupling prediction model.
Keywords: Coal and gas outburst, Feature selection, Boruta, Apriori, Bayesian optimization, SVM
