Volume 45 Issue 5
Oct.  2017
Turn off MathJax
Article Contents
Abstract
References
Related Articles
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
shu

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

  • College of Earth and Environment, Anhui University of Science and Technology, Huainan 232001, China

Funds: 

National Natural Science Foundation of China(51474008)

More Information
  • Received Date: September 09, 2016
  • Published Date: October 24, 2017
    Graphical Abstract
    • Abstract
  • 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.
    • FullText(HTML)
    • References (16)
  • [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.
    • Related Articles

  • Related Articles

    [1]SUN Fuxun, WEI Jiuchuan, WAN Yunpeng, LIU Chuane. Recognition method of mine water source based on Fisher's discriminant analysis and centroid distance evaluation[J]. COAL GEOLOGY & EXPLORATION, 2017, 45(1): 80-84. DOI: 10.3969/j.issn.1001-1986.2017.01.016
    [2]MA Lei, QIAN Jiazhong, ZHAO Weidong, ZHOU Xiaoping. GIS-based decision-making support system for prevention and control of water hazards in coal mines[J]. COAL GEOLOGY & EXPLORATION, 2014, 42(5): 44-49. DOI: 10.3969/j.issn.1001-1986.2014.05.009
    [3]SUN Ming, ZHENG Wenxiang, ZHANG Wenquan, MA Kai. Discrimination system of combined grey matter-element for water-bursting in seam floor of deep mine[J]. COAL GEOLOGY & EXPLORATION, 2011, 39(6): 36-40. DOI: 10.3969/j.issn.1001-1986.2011.06.009
    [4]ZHANG Chunlei, QIAN Jiazhong, ZHAO Weidong, MA Lei. The application of bayesian approach to discrimination of mine water-inrush source[J]. COAL GEOLOGY & EXPLORATION, 2010, 38(4): 34-37. DOI: 10.3969/j.issn.1001-1986.2010.04.008
    [5]SUN Ya-jun, YANG Guo-yong, ZHENG Lin. Distinguishing system study on resource of mine water inrush based on GIS[J]. COAL GEOLOGY & EXPLORATION, 2007, 35(2): 34-37.
    [6]YIN Xiao-xi, XU Guang-quan, GUI He-rong, CHEN Lu-wang. Analyzing for sources of inrush-water in Wanbei Mining Area by systemic clustering and stepwise distinguishing[J]. COAL GEOLOGY & EXPLORATION, 2006, 34(2): 58-61.
    [7]ZHANG Yong-tai, JIANG Zai-bing. The distinguishing model for the resource of water inrush from coal mine and the software system[J]. COAL GEOLOGY & EXPLORATION, 2003, 31(3): 35-37.
    [8]PENG Su-ping, DUAN Yan-e, MENG Zhao-ping, GE Lian-zhu. Application on Bayes stepwise discnminant analysis method in partition of mine district[J]. COAL GEOLOGY & EXPLORATION, 2003, 31(2): 10-12.
    [9]Li Caihui, Niu Xiangfeng, Ren Suzhen, Kong Fanping. A SYSTEM FOR FAST WATER QUALITY ANALYSIS AND FAST WATER SOURCE DISCRIMINATION[J]. COAL GEOLOGY & EXPLORATION, 1998, 26(3): 43-46.
    [10]Chen Chaoyang, Wang Jingming, Dong Shuning, Jiang Feng, Wang Qinglong, Gao Jianzhong. A MODEL OF DISCRIMINATION OF THE WATER SOURCES OF OUTBURST IN JIAOZUO COAL MINE AREA[J]. COAL GEOLOGY & EXPLORATION, 1996, 24(4): 38-40.

  • Cited by

    Periodical cited type(25)

    1. 郑剑，陈陆望，张杰，张苗，郑忻，胡永胜. 贝叶斯突水水源判别与反向水文地球化学模拟的含水层水力分析. 合肥工业大学学报(自然科学版). 2025(02): 203-211 .
    2. 蒋欣静，李仲夏，万军伟，成建梅，王志明，王玲，王铭. 柳家村隧洞水化学特征分析及涌水水源判别方法研究. 安全与环境工程. 2024(04): 158-169 .
    3. 张强，胡志伟，王毛毛，周成号. 基于主成分自组织神经网络法的测井曲线分层技术. 地质与勘探. 2024(05): 1013-1020 .
    4. 施龙青，曲兴玥，韩进. 黄土梁峁地貌矿井水水质时空变异评估与关键控制因子水源识别. 煤田地质与勘探. 2023(02): 195-206 . 本站查看
    5. 陈陆望，胡永胜，张杰，张苗，郑剑，郑忻，张媛媛，蔡欣悦，武明辉. 华北型煤田水文地球化学勘探关键技术研究进展. 煤田地质与勘探. 2023(02): 207-219 . 本站查看
    6. 赵世毫，高林，陈原望，蒋明，向天麟，申业兴，许帅. 基于水化学特征分析的近地表井壁涌水来源识别技术. 矿业研究与开发. 2023(11): 150-156 .
    7. 张苗，陈陆望，姚多喜，张杰. 宿县矿区石炭系太灰地下水化学特征及水岩相互作用. 合肥工业大学学报(自然科学版). 2022(03): 396-405 .
    8. 苏玮，姜春露，查君珍，郑刘根，谢华东，黄文迪，陈园平. 基于客观组合权-改进集对分析模型的矿井突水水源识别. 煤炭科学技术. 2022(04): 156-164 .
    9. 张胜军，丁亚恒，姜春露，李明，郑刘根. 基于水化学和氯同位素的煤矿矿井水来源识别与解析. 煤炭工程. 2022(06): 123-127 .
    10. 毛志勇，崔鹏杰，黄春娟，韩榕月. KPCA-CS-SVM下的矿井突水水源判别模型. 辽宁工程技术大学学报(自然科学版). 2021(02): 104-111 .
    11. 满孝全，魏久传，谢道雷，谢春雷. 基于水化学特征分析的突水水源判别方法. 中国科技论文. 2021(01): 76-81+90 .
    12. 朱敬忠，李凌，杨森. 基于因子分析的突水水源类型判别的研究. 矿业安全与环保. 2021(02): 87-91+96 .
    13. 施亚丽，吴基文，翟晓荣，王广涛，洪荒，毕尧山. 采动影响下皖北恒源煤矿八含水化学特征研究. 安徽理工大学学报(自然科学版). 2021(01): 31-40 .
    14. 苏俏俏，黄平华，丁风帆，胡永胜，郜鸿飞. 基于Piper-PCA-Fisher模型的矿井突水水源识别. 能源与环保. 2021(10): 122-127 .
    15. 李凌，胡友彪，刘瑜，琚棋定. 基于多元统计与Bayes判别模型的水源判别. 安徽理工大学学报(自然科学版). 2021(06): 25-31 .
    16. 刘小贺. 主成分-距离水质识别水源模型主成分的选取. 地下水. 2020(02): 23-26 .
    17. 姜子豪，胡友彪，琚棋定，周露，张淑莹. 矿井突水水源判别方法. 工矿自动化. 2020(04): 28-33 .
    18. 马济国，姜春露，朱赛君，谢毫，毕波，郑刘根. 基于主成分分析的潘谢矿区突水水源Fisher判别模型. 煤炭技术. 2020(09): 132-134 .
    19. 张靖苑. 基于主成分分析和随机配置网络的矿井突水水源判别方法研究. 煤炭工程. 2020(S2): 101-104 .
    20. 题正义，张峰，秦洪岩，朱志洁. 基于板壳和断裂力学理论的上覆采空区积水危险性判定技术. 煤田地质与勘探. 2019(01): 138-143 . 本站查看
    21. 刘国伟，马凤山，郭捷，杜云龙，侯成录，李威. 多元统计分析在滨海矿区水源识别中的应用——以三山岛金矿为例. 黄金科学技术. 2019(02): 207-215 .
    22. 董东林，李祥，林刚，卞建玲，曹成龙，吴恒. 突水水源的独立性权–模糊可变理论识别模型. 煤田地质与勘探. 2019(05): 48-53 . 本站查看
    23. 张宇，彭琪，曾开帅，唐金平，何文君，张强. Bayes判别理论在陇西黄土高原地区地下水化学分类中的应用. 甘肃水利水电技术. 2019(10): 1-5 .
    24. 邓松松. 超化煤矿突水灾害地质条件分析及防治措施. 能源与节能. 2018(06): 38-39 .
    25. 琚棋定，胡友彪，张淑莹. 基于主成分分析与贝叶斯判别法的矿井突水水源识别方法研究. 煤炭工程. 2018(12): 90-94 .

    Other cited types(16)

Catalog

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (134) PDF downloads (15) Cited by(41)
    Related

    Copyright of website design © Editorial Office of Coal Geology & Exploration 陕ICP备05001372号-6 陕公网安备 61019002002193号

    Address: Editorial Office of Coal Geology & Exploration, No.82 Jinye 1st Rd, High-Tech Zone, Xi'an City, Shaanxi, China

    Tel: 029-81778075 E-mail: ccrimtdzykt@vip.163.com

    Supported by: Beijing Renhe Information Technology Co., Ltd.  Baidu Analytics

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return