Gas outburst prediction based on rough set and particle swarm optimization support vector machine

In view of coal and gas outburst intensity forecast problems in coal mines, on the basis of comprehensive influence factors of gas outburst, a decision table of gas outburst intensity was established by employing the rough set theory and support vector machine, and selecting coal thickness variations, geological structures, coefficient of the solid coal, roadway pressure, gas change, gas desorption value of drilling chip, and ten main influence. Using the attribute reduction algorithm in rough set theory to eliminate redundant information, and particle swarm optimization to optimize parameters of Support Vector Machine, the main control factors of gas outburst were mapped to high-dimensional space through kernel function, and the nonlinear relationship between main control factors and intensity of gas outburst was fitted. A gas outburst prediction model based on rough set theory and particle swarm optimization support vector machine was established. A typical example of gas outburst was selected as a study sample, and a prominent example of a mine in Henan was used as a test sample for prediction. The experimental results show that the model can meet the requirements of gas outburst prediction, with the prediction results being consistent with the actual results.