A forecasting and forewarning model for methane hazard in working face of coal mine based on LS-SVM |
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Authors: | CAO Shu-gang LIU Yan-bao WANG Yan-ping |
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Affiliation: | 1. Key Laboratory for the Exploitation of Southwest Resources & the Environmental Disaster Control Engineering, Ministry of Education, Chongqing University, Chongqing 400044, China 2. College of Petroleum Engineering, Chongqing University of Science and Technology, Chongqing 400042, China |
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Abstract: | To improve the precision and reliability in predicting methane hazard in working face of coal mine, we have proposed a forecasting and forewarning model for methane hazard based on the least square support vector (LS-SVM) multi-classifier and regression machine. For the forecasting model, the methane concentration can be considered as a nonlinear time series and the time series analysis method is adopted to predict the change in methane concentration using LS-SVM regression. For the forewarning model, which is based on the forecasting results, by the multi-classification method of LS-SVM, the methane hazard was identified to four grades: normal, attention, warning and danger. According to the forewarning results, corresponding measures are taken. The model was used to forecast and forewarn the K9 working face. The results obtained by LS-SVM regression show that the forecast- ing have a high precision and forewarning results based on a LS-SVM multi-classifier are credible. Therefore, it is an effective model building method for continuous prediction of methane concentration and hazard forewarning in working face. |
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Keywords: | working face methane concentration LS-SVM forecasting forewarning |
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