Forecasting Coal and Gas Outburst Based on Support Vector Machine

Support vector machine (SVM) is a novel machine learning method based on statistical learning theory (SLT). SVM is powerful for the problem with small sample, non linear and high dimension. A multi-class SVM classifier is applied to predict the coal and gas outburst in the paper. In this model, the dominant factors are the input vectors and the degree of outburst danger is divided into four types: heavy outburst, common outburst, outburst warning and no existing outburst. Through a special data dealing process, the multi-class SVM classifier, trained with the sampling data, identifies out the four types of coal and gas outburst states. An empirical analysis shows that some perfect computing conclusions have been acquired by the proposed model.