Abstract: Currently,the traditional prediction algorithms for gas concentration both domestically and internationally primarily rely on ARIMA and SVM models.With the rapid development of deep learning technology and the rise of neural networks,the la-test gas concentration prediction is conducted through recurrent neural network(RNN) models.Due to their nonlinear characteris-tics and consideration of data connections,RNNs have further improved the prediction performance compared to traditional prediction algorithms.However,as the length of the sample sequence increases,the prediction ability decreases due to inherent flaws in the model.In response to this issue,the paper proposes a novel gas concentration prediction model.This model combines con-volutional neural networks (CNNs) with RNNs and incorporates an attention mechanism to enhance the expressive power between data.Through testing using actual data from the 1209 working face of Zhongxing Coal Industry in Shanxi Fenxi Mining Group,the average relative error predicted by the traditional RNN model is 0.42 1,while the average relative error predicted by the proposed model is 0.029 3.The experiment demonstrates that the proposed algorithm achieves better prediction performance compared to traditional gas concentration prediction algorithms.

Key words: Gas concentration prediction, Deep learning, Convolutional neural network, Recurrent neural network, Attention mechanism, LSTM