Coal mining gas emission constrained by many factors, considering the eight main factors of gas emission. The first gas emission data are normalized, avoid data overflow to improve the training speed of neural network. Then use BP neural network to predict the amount of mine gas emission, finally proposed gas emission control measures.
The research on predicting gas outburst hazard on the basis of the unbalanced problem of gas monitoring samples under usual circumstances is given in this paper. Combined with outburst-preventing monitoring parameters, a new kind of method of predicting gas outburst hazard based on v-SVM algorithm through analyzing features of real-time gas monitoring data and extracting parameters of gas concentration real-time variation trend, parameters of gas variation rates and feature parameters of gas emission is put forward in this paper. And the application shows that this algorithm for the prediction of actual gas emission in coal mines is effective.