ZHANG Hao, YAO Duoxi, LU Haifeng, ZHU Ningning, XUE Liang. Application of principal component analysis and Bayes discrimination approach in water source identification[J]. COAL GEOLOGY & EXPLORATION, 2017, 45(5): 87-93. DOI: 10.3969/j.issn.1001-1986.2017.05.016
Citation: ZHANG Hao, YAO Duoxi, LU Haifeng, ZHU Ningning, XUE Liang. Application of principal component analysis and Bayes discrimination approach in water source identification[J]. COAL GEOLOGY & EXPLORATION, 2017, 45(5): 87-93. DOI: 10.3969/j.issn.1001-1986.2017.05.016

Application of principal component analysis and Bayes discrimination approach in water source identification

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National Natural Science Foundation of China(51474008)

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  • Received Date: September 09, 2016
  • Published Date: October 24, 2017
  • Accurate and efficient determination of water inrush source is the prerequisite for solution of mine water disaster. In order to solve the problem of mine water disaster, based on water quality analysis data of 59 water samples of different aquifers in Yuaner coal mine, principal component analysis in multivariate statistical analysis was used. Principal component analysis was used to calculate the factor score of different water samples, hierarchical clustering was conducted and the erroneous samples were rejected. The remaining water samples were used as learning samples to test the accuracy of determination of Bayes discriminant function. It is concluded that the accuracy was 92.5%. The discriminant results were verified by cross-validation. The discriminant function was used to discriminate the water sample from the water-rich area of a working face floor in the mine, and the results coincided with the actual situation. The results show that the discrimination method based on principal component analysis and Bayes discrimination method was more accurate than single Bayes discrimination method, could eliminate the interaction effect among variables of samples and realize fast and effective discrimination of water inrush source, the discriminant process of water inrush source is simple and the accuracy is high. The interaction between the sample of variables was eliminated, was realized. It provides effective basis for coal mine water prevention and control.
  • [1]
    陈红江,李夕兵,刘爱华. 矿井突水水源判别的多组逐步Bayes判别方法研究[J]. 岩土力学,2009,30(12):3655-3659.CHEN Hongjiang,LI Xibing,LIU Aihua. Studies of water source determination method of mine water inrush based on Bayes multi-group stepwise discriminant analysis theory[J]. Rock and Soil Mechanics,2009,30(12):3655-2659.
    [2]
    郭江峰,姚多喜,黄河. 基于Bayes算法的煤矿井下突水水源判识系统的设计与实现[J]. 水文地质工程地质,2016,43(2):153-158.

    GUO Jiangfeng,YAO Duoxi,HUANG He. System design and implementation of water source identification of mine water inrush based on the Bayes Algorithm[J]. Hydrogeology and Engineering Geology,2016,43(2):153-158.
    [3]
    宫凤强,鲁金涛. 基于主成分分析与距离判别分析法的突水水源识别方法[J]. 采矿与安全工程学报,2014,31(2):236-242.

    GONE Fengqiang,LU Jintao. Recognition method of mine water inrush sources based on the principal element analysis and distance discrimination analysis[J]. Journal of Mining and Safety Engineering,2014,31(2):236-242.
    [4]
    鲁金涛,李夕兵,宫凤强. 基于主成分分析与Fisher判别分析法的矿井突水水源识别方法[J]. 中国安全科学学报,2012,22(7):109-115.

    LU Jintao,LI Xibing,GONG Fengqiang. Recognizing of mine water inrush sources based on principal components analysis and fisher discrimination analysis method[J]. China Safety Science Journal,2012,22(7):109-115.
    [5]
    牟林. 水质动态曲线预测在突水水源判别中的应用[J]. 煤田地质与勘探,2016,44(3):70-74.

    MOU Lin. Application of dynamic curve prediction method in discriminating water-bursting source[J]. Coal Geology & Exploration 2016,44(3):70-74.
    [6]
    王江荣,黄建华,罗资琴,等. 基于粗糙集的 Logistic 回归模型在矿井突水模式识别中的应用[J]. 煤田地质与勘探,2015,43(6):70-74.

    WANG Jiangrong,HUANG Jianhua, LUO Ziqin,et al. Application of Logistic regression model based on rough set in recognition of mine water inrush pattern[J]. Coal Geology & Exploration,2015,43(6):70-74.
    [7]
    吕纯. 谢一矿地下水化学特征及突水水源判别Elman神经网络模型[D]. 合肥:合肥工业大学,2009:34-35.
    [8]
    邓清海,曹家源,张丽萍,等. 基于主成分分析的矿井突水水源Bayes判别模型[J]. 水文地质工程地质,2014,41(6):20-25.

    DENG Qinghai,CAO Jiayuan,ZHANG Liping. The Bayesian discrimination model for sources of mine water inrush based on principal components analysis[J]. Hydrogeology and Engineering Geology,2014,41(6):20-25.
    [9]
    李彬,史海滨,李桢,等. 基于Bayes判别理论的地下水化学分类的分析方法[J]. 干旱地区农业研究,2015,33(4):246-250.

    LI Bin,SHI Haibin,LI Zhen,et al. An analyzing method for chemical classifications of groundwater based on the Bayes discriminant theory[J]. Agricultural Research in the Arid Areas,2015,33(4):246-250.
    [10]
    黎锦贤,胡千庭. 主成分分析法在煤矿安全评价中的应用[J]. 矿业安全与环保,2007,34(5):71-76.

    LI Jinxian,HU Qianting. Application of principal component analysis in coal mine safety evaluation[J]. Mining Safety and Environmental Protection,2007,34(5):71-76.
    [11]
    江晓益,成春奇. 矿井地下水系统水质分类判别的多元统计分析[J]. 水文地质工程地质,2009,11(4):16-20.

    JIANG Xiaoyi,CHENG Chunqi. Hydrochemical classification and identification of groundwater in mining region using multivariate statistical analysis[J]. Hydrogeology and Engineering Geology,2009,11(4):16-20.
    [12]
    曹雪春,钱家忠,孙兴平. 煤矿地下水系统水质分类判别的多元统计组合模型—以顾桥矿为例[J]. 煤炭学报,2010,35(增刊1):141-144.

    CAO Xuechun,QIAN Jiazhong,SUN Xingping. Hydrochemical classification and identification for groundwater system by using integral multivariate statistical models: a case study in Guqiao Mine[J]. Journal of China Coal Society,2010,35(S1):141-144.
    [13]
    张文彤. 世界优秀统计工具SPSS11.0统计分析教程(高级篇)[M]. 北京:北京希望电子出版社,2002:166-210.
    [14]
    王力宾. 多元统计分析:模型案例及SPSS应用[M]. 北京:经济科学出版社,2010:132-250.
    [15]
    沈慧珍,许光泉. 多元统计方法在矿井地下水中的应用[J]. 安徽理工大学学报(自然科学版),2002,24(增刊1):1-5.

    SHEN Huizhen,XU Guangquan. Application of multiple statistics analysis method in mine water[J]. Journal of Anhui University of Science and Technology(Natural Science),2002,24(S1):1-5.
    [16]
    张春雷,钱家忠,赵卫东,等. Bayes方法在矿井突水水源判别中的应用[J]. 煤田地质与勘探,2010,38(4):34-37.

    ZHANG Chunlei,QIAN Jiazhong,ZHAO Weidong,et al. The application of bayesian approach to discrimination of mine water-inrush source[J]. Coal Geology and Exploration,2010,38(4):34-37.
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