Research and Application of Improved Gas Concentration Prediction Model Based on Grey Theory and BP Neural Network in Digital Mine

Abstract

Gas disaster is one of the most serious disasters in coal mine safety production. Therefore, it is of great significance to strengthen the coal mine gas disasters forecasting warning technology research for improving the ability of prevention and control gas disaster in coal mine and promoting the development of digital mine in our country. The biggest characteristics of using grey prediction model GM (1,1) is that the algorithm is quite simple, and also, when building the model, less data can be used. It is convenient for modelling and operation, but the effect of forecast of the grey prediction model for systems with volatility is not very ideal, and the prediction accuracy will reduce gradually along with the extrapolation of time. BP neural network has a good performance for prediction of nonlinear system, but when the network was trained, it often requires large amounts of data. This paper is based on the grey prediction model, using advantages of grey prediction that the model algorithm is simple and the procedure of model building needs less data, and the BP neural network that the performance of grey forecast model for nonlinear system prediction is good. We revise the grey prediction model through BP neural network and finally we build an improved gas concentration prediction model based on grey theory and BP neural network, and carry out a specific computer simulation. Results show that the model effectively improved the precision of gas prediction.