A forecasting and forewarning model for methane hazard in working face of coal mine based on LS-SVM

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 forecasting 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.