@article{ref1,
title="Study on PSO-BP neural network prediction method of coal seam gas content and its application",
journal="China safety science journal (CSSJ)",
year="2020",
author="Lin, H. and Gao, F. and Yan, M. and Bai, Y. and Xiao, P. and Xie, X.",
volume="30",
number="9",
pages="80-87",
abstract="In order to make more scientific and accurate prediction of coal seam gas content, a predication model was proposed based on PSO-BP neural network. Firstly, relationship between gas content and buried depth, seam thickness, floor elevation and vertical distance from fault to measuring points were analyzed. Then, prediction results of this model, multiple linear regression model, and BP neural network model were compared and analyzed. The results show that as burial depth, seam thickness, and vertical distance grow, gas content increases, but it will decrease when floor elevation increases.And relative error of PSO-BP neural network model is 2.4%-4.8% (3.1% on average), that of multiple linear regression model is 2.3%-77.4% (27.7% on average), and BP neural network is 7.5%-14.8% (10.2% on average), showing PSO-BP model has the highest prediction accuracy. © 2020 China Safety Science Journal. All rights reserved.<p /><p>Language: zh</p>",
language="zh",
issn="1003-3033",
doi="10.16265/j.cnki.issn.1003-3033.2020.09.012",
url="http://dx.doi.org/10.16265/j.cnki.issn.1003-3033.2020.09.012"
}