Research on prediction of gas emission in working face based on WOA-LSTM

In order to improve the scientificity and accuracy of gas emission prediction in coal mine, this paper put forward a multi-step gas emission prediction model of whale optimization algorithm (WOA)- Long short-term memory network(LSTM). The model first used Pearson correlation coefficient method to analyze the characteristics of the influencing factors of gas emission, and selected 9 main characteristics affecting gas emission as the external input features of the model. Then, the optimal parameters were obtained by using whale optimization algorithm to optimize the number of hidden layer neurons, batch number and time step of LSTM neural network. Finally, a WOA-LSTM model was constructed to predict the value of gas emission. The prediction accuracy of the model under different time steps and the prediction effects of LSTM, RNN and BP model were compared and analyzed experimentally. The experimental results show that the multi-step prediction model of gas emission based on WOA-LSTM reaches the minimum error value in three time steps. Its average absolute error is reduced by 41.6%, 46.6% and 65.8% respectively compared with LSTM, RNN and BP neural network models. Moreover, the model has strong robustness and can provide reference for mine gas prevention and control.