Periodic pressure prediction of working face based on dynamic adaptive sailfish optimization BP neural network

In order to solve the problems of insufficient precision, poor generalization, and high computational requirements of existing methods for periodic pressure prediction of working face, a periodic pressure prediction model of working face based on dynamic adaptive sailfish optimization BP neural network (DASFO-BP) is proposed. By analyzing the mechanism of working face periodic pressure, the influencing factors related to pressure are obtained. The Pearson correlation coefficient is used to determine the factors that have a significant impact on pressure (advance speed, direct roof thickness, basic roof thickness, mining height, coal seam dip angle, and dip length) as inputs for the prediction model. The subsequent pressure intensity and pressure step distance are used as outputs for the prediction model. A dynamic adaptive optimization strategy is proposed to improve the robustness of the sailfish optimization (SFO) algorithm. In the early stage of optimization, SFO is used to achieve fast convergence, while in the middle stage, bald eagle search (BES) is used to escape local optima. In the later stage, the advantage of particle swarm optimization (PSO) deep search is utilized to improve the precision of the solution. A dynamic adaptive sailfish optimization (DASFO) algorithm is improved to train the hyperparameters of the BP neural network, and a pressure prediction model based on DASFO-BP is constructed. The experimental results indicate that the DASFO algorithm can achieve fast convergence on both unimodal and multimodal test functions. Compared with BP, SFO-BP, and NCPSO-BP, DASFO-BP has higher precision in predicting the intensity and step distance of periodic pressure, and has strong generalization ability and fitting capability. It can accurately predict the pressure and its distribution in the next period.