Application of Rough Set and Support Vector Machine to Prediction of Coal and Gas Outburst

A prediction method of coal and gas outburst was presented based on the combination of attribute reduction function of rough set theory and nonlinear mapping characteristics of support vector machine. Firstly, attribute reduction and denoising were executed. Secondly, the training samples that have been processed were input to the support vector machine to train the model. Finally, the trained model was used to predict the testing samples. Practical application demonstrates that: (1) Gas pressure, gas emission rate, geological structure, protodyakonov coefficient of coal and mining depth are the indispensable indexes of coal and gas outburst. (2) The prediction model based on rough set and support vector machine has high precision and good practicability, and is a very efficient method for predicting coal and gas outburst.