Gas emergence prediction based on a combination of improved dung beetle optimizer algorithm and temporal convolutional network

In order to better analyze and process the coal mine gas outflow data, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is proposed to decompose the gas outflow sequence and construct the time convolution network (TCN) model for each component separately. The relevant hyperparameters of the TCN model are optimally found using the Improved Dung Beetle optimizer (IDBO) algorithm, and the predicted output values of each component are superimposed to obtain the final predicted value of gas outflow. The proposed CEEMDAN-IDBO-TCN combined prediction method can provide high accuracy gas outflow prediction.