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

doi:10.16265/j.cnki.issn1003-3033.2017.03.018

Abstract

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

CLC Number:

X936

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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