Research on coalbed gas content prediction method based on LSSA-BP neural network

Abstract

In order to improve the accuracy and reliability of coal seam gas content prediction, this paper proposed the coal seam gas content prediction model (LSSA-BP model) based on Logistic chaotic mapping improved sparrow search algorithm to optimize BP neural network. Firstly, the main control factors of gas content were selected using grey correlation analysis (GRA) as the node number of the input layer of the LSSA-BP prediction model. Then, the sparrow population was initialized by Logistic chaotic mapping to increase the diversity of the population. The problems of slow convergence rate and easy to fall into local minimum of single BP model are solved. Through model application, the prediction results of LSSA-BP, SSA-BP and BP models are compared. The results show that: the mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE) and mean absolute percentage error (MAPE) of LSSA-BP prediction model were 0.346 9 m3/t, 0.172 1 m3/t, 0.414 9 m3/t and 27.4036%, respectively, which were better than other models. The accuracy and stability of coal seam gas content prediction are improved.

Keywords

• coalbed gas content
• bp neural network
• sparrow search algorithm (ssa)
• logistic chaos mapping
• grey correlation analysis (gra)