@article{Li2025, 
author = {Gang Li and Yiming Zhao and Qinghe Yang and Tian Cai and Junpeng Zou},
title = {Failure Depth Prediction of Coal Seam Floor Based on SA-PSO-BP Neural Network},
year = {2025},
journal = {Chinese Journal of Underground Space and Engineering},
volume = {21},
number = {1},
pages = {293-299},
keywords = {mining under pressure, failure depth of seam floor, neural network prediction, SA-PSO-BP neural network},
url = {https://www.sciopen.com/article/10.20174/j.JUSE.2025.01.32},
doi = {10.20174/j.JUSE.2025.01.32},
abstract = {It is of great significance to study the accurate prediction of the failure depth of coal seam floor to ensure the safety production of coal mine under the condition of mining under pressure. A new SA-PSO-BP neural network model is proposed to solve the problems of large error, easy to fall into local optimal solution and slow convergence speed in the traditional BP neural network prediction of floor failure depth. The model takes the coal seam dip angle, mining depth, coal seam mining thickness and the inclined length of working face as the evaluation indexes, in order to avoid PSO falling into local optimal solution, a simulated annealing algorithm (SA) was introduced to improve the optimization process of BP neural network by using Particle swarm optimization (PSO), the optimized model is trained and predicted. The results show that the goodness of fit of SA-PSO-BP neural network model is 0.9835, which is 0.2882 higher than that of BP neural network, and the root mean square error is 1.3190, which is 3.8641 lower than that of BP neural network. The average absolute percentage error is 5.4423, which is 14.93% less than that of BP neural network. The SA-PSO-BP network model is feasible, and it provides a reasonable method for the prediction of floor failure depth.}
}