Experimental study on prediction of gas pressure variation during coal and gas outburst Online publication date: Mon, 02-Dec-2024
by Erhui Zhang; Xukai Dong; Baokun Zhou; Lei Yang
International Journal of Oil, Gas and Coal Technology (IJOGCT), Vol. 37, No. 1, 2025
Abstract: To accurately predict the variation of gas pressure during the coal and gas outburst experiment, a gas pressure prediction model was established based on Keras and long short-term memory (LSTM). Meanwhile, the ARMA and ARIMA models were selected for comparative analysis. The findings reveal that the ARMA model exhibits the shortest prediction time, but its RMSE and MAE values are the largest, suggesting that the ARMA model yields the poorest predictive performance. The LSTM model achieved the lowest RMSE, and its MAE closely approached that of ARIMA, but the ARIMA model could only predict the gas pressure in the short term. Therefore, it can be employed as the prediction model for gas pressure in coal and gas outburst experiments. The research findings offer significant auxiliary support for predicting the change of gas pressure during coal and gas outbursts, thereby facilitating further prediction and prevention of such occurrences. [Received: October 9, 2023; Accepted: February 27, 2024]
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