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.
引用
收藏
页数:19
相关论文
共 50 条
  • [31] Control effect of the position of key stratum on the height of water flowing fractured zone
    Bu, Wankui
    Electronic Journal of Geotechnical Engineering, 2013, 18 M : 2617 - 2624
  • [32] Height and detection of water flowing fractured zone in fully mechanized mining area
    Xu, Baishan
    Li, Qi
    Li, Zhihong
    Hao, Zhijian
    Gao, Guojun
    NEAR-SURFACE GEOPHYSICS AND GEOHAZARDS - PROCEEDINGS OF THE 4TH INTERNATIONAL CONFERENCE ON ENVIRONMENTAL AND ENGINEERING GEOPHYSICS, VOLS 1 AND 2, 2010, : 141 - 145
  • [33] Towards Predicting Water Levels Using Artificial Neural Networks
    Londhe, Shreenivas N.
    OCEANS 2009 - EUROPE, VOLS 1 AND 2, 2009, : 1223 - 1228
  • [34] Predicting water saturation using artificial neural networks (ANNS)
    Al-Bulushi, Nabil
    Araujo, Mariela
    Kraaijveld, Martin
    PROCEEDINGS OF THE IASTED INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND APPLICATIONS, 2007, : 57 - +
  • [36] Stepped development characteristic of water flowing fracture height with variation of mining thickness
    Wang X.
    Xu J.
    Han H.
    Ju J.
    Xing Y.
    Meitan Xuebao/Journal of the China Coal Society, 2019, 44 (12): : 3740 - 3749
  • [37] Case Study for Predicting Failures in Water Supply Networks Using Neural Networks
    de Sousa Medeiros, Viviano
    dos Santos, Moises Dantas
    Brito, Alisson Vasconcelos
    WATER, 2024, 16 (10)
  • [38] Neural network-based prediction methods for height of water-flowing fractured zone caused by underground coal mining
    Dai, Song
    Han, Bo
    Liu, Shiliang
    Li, Ningbo
    Geng, Fei
    Hou, Xizhong
    ARABIAN JOURNAL OF GEOSCIENCES, 2020, 13 (12)
  • [39] Predicting the height of the water-conducting fractured zone using multiple regression analysis and GIS
    Liu, Yong
    Yuan, Shichong
    Yang, Binbin
    Liu, Jiawei
    Ye, Zhaoyong
    ENVIRONMENTAL EARTH SCIENCES, 2019, 78 (14)
  • [40] Predicting the height of the water-conducting fractured zone using multiple regression analysis and GIS
    Yong Liu
    Shichong Yuan
    Binbin Yang
    Jiawei Liu
    Zhaoyong Ye
    Environmental Earth Sciences, 2019, 78