Prediction of gas concentration in fully mechanized mining face based on LSTM model based on time series

Abstract

Gas disaster has always been a major safety problem in the coal mine field. The prediction of gas concentration in fully mechanized mining face is of great significance to ensure the safety of mine production and the safety of underground personnel. A Long short-term Memory (LSTM) neural network model based on time series is proposed for the prediction of gas concentration. Since there are many factors affecting the gas emission and there is a complex nonlinear relationship between them, a method of data preprocessing is proposed to weaken the data volatility, combined with the powerful GPU function of the computer, to build an LSTM neural network in the Tensorflow environment Gas Emission Prediction Model, using root mean square error (RMSE) and running time, for evaluating forecast performance. The prediction results are compared with the SVR network, and the results show that the LSTM model has higher prediction accuracy and prediction stability.