Mine Gas Concentration Forecasting Model Based on an Optimized BiGRU Network

Abstract

To improve the utilization of mine gas concentration monitoring data with deep learning theory, we propose a gas concentration forecasting model with a bidirectional gated recurrent unit neural network (Adamax-BiGRU) using an adaptive moment estimation maximum (Adamax) optimization algorithm. First, we apply the Laida criterion and Lagrange interpolation to preprocess the gas concentration monitoring data. Then, the MSE is used as the loss function to determine the parameters of the hidden layer, hidden nodes, and iterations of the BiGRU model. Finally, the Adamax algorithm is used to optimize the BiGRU model to forecast the gas concentration. The experimental results show that compared with the recurrent neural network, LSTM, and gated recurrent unit (GRU) models, the error of the BiGRU model on the test set is reduced by 25.58, 12.53, and 3.01%, respectively. Compared with other optimization algorithms, the Adamax optimization algorithm achieved the best forecasting results. Thus, Adamax-BiGRU is an effective method to predict gas concentration values and has a good application value.