PCA-BP神经网络模型预测导水裂隙带高度

doi:10.16265/j.cnki.issn1003-3033.2017.03.018

中国安全科学学报 ›› 2017, Vol. 27 ›› Issue (3): 100-105.doi: 10.16265/j.cnki.issn1003-3033.2017.03.018

PCA-BP神经网络模型预测导水裂隙带高度

谢晓锋¹, 李夕兵¹ 教授, 尚雪义¹, 翁磊¹, 邓青林^1,2

1 中南大学资源与安全工程学院,湖南长沙 410083
2 核工业西南勘察设计研究院有限公司,四川成都 610000

收稿日期:2016-11-25 修回日期:2017-01-12 发布日期:2020-11-22
作者简介:谢晓锋 (1988—),男,湖南宁乡人,博士研究生,研究方向为岩石力学与岩土工程。E-mail:xxfeng@csu.edu.cn。
基金资助:
国家自然科学基金资助(41630642);国家重点研发计划(2016YFC0600706);中南大学中央高校基本科研业务费专项资金资助(2015zzts084)。

Prediction of height of water flowing fractured zone based on PCA-BP neural networks model

XIE Xiaofeng¹, LI Xibing¹, SHANG Xueyi¹, WENG Lei¹, DENG Qinglin^1,2

1 School of Resources and Safety Engineering, Central South University, Changsha Hunan 410083, China
2 Nuclear Industry Southwest Survey & Design Institute Co., Ltd, Chengdu Sichuan 610000, China

Received:2016-11-25 Revised:2017-01-12 Published:2020-11-22

摘要/Abstract

摘要： 导水裂隙带高度的预测对煤矿安全开采有重要意义,而传统回归方法未考虑因素间相关系数对预测结果的影响。选取采深、煤层倾角、煤层厚度、煤层硬度、岩层结构、顶板岩石单轴抗压强度、开采厚度和采空区斜长作为预测导水裂隙带高度的影响因素,建立基于PCA-BP神经网络的导水裂隙带高度预测模型。测试结果表明,煤层厚度对导水裂隙带高度的影响最大,其余各因素对导水裂隙带高度的影响较大,采深和开采厚度对导水裂隙带高度的影响较小;PCA-BP神经网络模型的训练速度和预测效果均优于BP神经网络模型,且最大预测误差仅为5.58%。

关键词: 导水裂隙带高度; 主成分分析(PCA); 神经网络; 影响因素; 相关系数

Abstract: The prediction of the height of water flowing fractured zone is of great importance for coal mining safety. However, traditional regression methods haven't considered the influence of correlation coefficients between factors on prediction performance. In this paper, the mining depth, coal seam inclination angle, coal seam thickness, coal seam hardness, rock structure, the uniaxial compressive strength of rock, mining thickness and goaf plagioclase were identified as the influencing factors for height forecast of water flowing fractured zone. Then a PCA-BP neutral network was developed to forecast the height of water flowing fractured zone. Results show that the coal seam thickness has the greatest influence on the height forecast of water flowing fractured zone, while the mining depth and mining thickness have a smaller influence, and the others have a middle influence,and that the speed of convergence and prediction accuracy of the PCA-BP neutral network are both better than that of the BP neutral network with the highest prediction error of 5.58%.

Key words: height of water flowing fractured zone, principle component analysis(PCA), neutral networks, influencing factor, correlation coefficient

中图分类号:

X936

谢晓锋, 李夕兵, 尚雪义, 翁磊, 邓青林. PCA-BP神经网络模型预测导水裂隙带高度[J]. 中国安全科学学报, 2017, 27(3): 100-105.

XIE Xiaofeng, LI Xibing, SHANG Xueyi, WENG Lei, DENG Qinglin. Prediction of height of water flowing fractured zone based on PCA-BP neural networks model[J]. China Safety Science Journal, 2017, 27(3): 100-105.

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PCA-BP神经网络模型预测导水裂隙带高度

Prediction of height of water flowing fractured zone based on PCA-BP neural networks model

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