Research on a Mine Gas Concentration Forecasting Model Based on a GRU Network

Abstract

To improve the level of safety in coal mine production, it is important to enhance the accuracy of coal mine gas concentration prediction. In the context of deep learning, we proposed a mine gas concentration prediction model based on gated recurrent units (GRUs). The GRU model is not only simple in structure but also offers high prediction accuracy, and it can make full use of the time-series characteristic of mine gas concentration data. First, we apply the Pauta criterion and Lagrange interpolation to preprocess mine gas concentration monitoring data. Then, a spatial reconstruction method is used to construct the training set for the prediction model. Finally, the mean square error (MSE) is used as the loss function and adaptive moment estimation (Adam) is used as the optimization algorithm to determine the learning parameters of the GRU model for predicting gas concentration values. Experimental results show that compared with models based on support vector regression (SVR), a backpropagation neural network (BPNN), a recurrent neural network (RNN) and a long short-term memory (LSTM) network, the proposed GRU-based model for gas concentration prediction achieves reduced error on the test set, and moreover, the GRU model is more efficient than the LSTM model in terms of run time. Thus, the accuracy and efficiency of gas concentration prediction are both improved, showing that the proposed model is of high practical value.

Keywords

• Deep learning
• gated recurrent unit
• gas concentration
• prediction