Prediction of gas emission based on grey-generalized regression neural network

Abstract

Abstract Coal mine gas is one of the main factors endangering mine safety in China, and the amount of gas emission is affected by a variety of related factors, which have the characteristics of non-linearity and uncertainty. In order to predict gas emission more quickly and accurately, a coupling algorithm of grey theory (GM (1,1) and generalized regression neural network (GRNN) is proposed. Thirteen parameters, such as coal seam gas content, depth and GM (1,1) gas emission prediction value, are used as input of the model. The input parameters are normalized and used as training and testing samples of the model. The 10 fold cross validation and minimum root mean square error (RMSE) are used to find the optimal smoothing factor (spread), then a non-linear prediction model of gas emission is established. The gas monitoring data of Qianjiaying Mining Area of Kailuan Mining Group from May 2007 to December 2008 are used in the experiment. The results show that the model has a great improvement in convergence speed and a good accuracy, which can provide theoretical basis for the prevention and control of coal mine gas disasters.