Predicting Water Flowing Fracture Zone Height Using GRA and Optimized Neural Networks

被引:0
作者
Dong, Haofu [1 ]
Yang, Genfa [1 ]
Guo, Keyin [1 ]
Xu, Junyu [2 ]
Liu, Deqiang [1 ]
Han, Jin [1 ]
Shi, Dongrui [1 ]
Pan, Jienan [2 ]
机构
[1] Gansu Huating Coal & Elect Co Ltd, Dongxia Coal Mine, Pingliang 774000, Peoples R China
[2] Henan Polytech Univ, Sch Resources & Environm, Jiaozuo 454000, Peoples R China
关键词
grey relational analysis; particle swarm optimisation; backpropagation neural network; the water-flowing fracture zone; MODEL; MINE; COAL;
D O I
10.3390/pr12112513
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
As coal mining depths continue to rise, consideration of WFFZ elevations is becoming increasingly important to mine safety. The goal was to accurately predict the height of the WFFZ to effectively prevent and manage possible roof water catastrophes and ensure the ongoing safety of the mine. To achieve this goal, we combined the particle swarm optimisation (PSO) algorithm with a backpropagation neural network (BPNN) in order to enhance the accuracy of the forecast. The present study draws upon the capacity of the PSO algorithm to conduct global searches and the nonlinear mapping capability of the BPNN. Through grey relational analysis (GRA), the order of the correlation degree was as follows: mining thickness > mining depth > overburden structure > mining width > mining dip. GRA has identified the degree of correlation between five influencing factors and the height of the WFFZ, among these, mining thickness, mining depth, overburden structure and mining width all show strong correlations, and the mining dip of the coal seam shows a good correlation. The weight ranking obtained by the PSO-BPNN method was the same as that obtained by the GRA method. Based on two actual cases, the relative errors of the obtained prediction results after PSO implementation were 2.97% and 3.47%, while the relative errors of the BPNN before optimisation were 18.46% and 4.34%, respectively, indicating that the PSO-BPNN method provides satisfactory prediction results and demonstrating that the PSO-optimised BPNN is easy to use and yields reliable results. In this paper, the height of the WFFZ model under the influence of five factors is only established for the Northwest Mining Area. With the continuous progress of technology and research, the neural network can consider more factors affecting the height of hydraulic fracturing development zones in the future to improve the comprehensiveness and accuracy of prediction.
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页数:19
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