Abstract
Accurate and effective identification of the source mine water inrush water is vital for warning system implementation and post-disaster rescue decision-making and is crucial for mine water disaster prevention plans. To fully excavate the hydrogeological information carried by the water samples of different water sources, and fully consider the influence of the correlation between the ions and the existence of multi-collinearity on the discrimination results, so as to improve the accuracy of the inrush water source discrimination, this study conducted multivariate statistical analysis and discriminant analysis on 37 water samples of three types of water sources in Xutuan coal mine, and established the Fisher discriminant model for mine water inrush sources based on fuzzy cluster analysis and factor analysis. The discriminant accuracy of the model was tested using re-substitution and cross-validation, and compared with the discriminant result of traditional Fisher discriminant model. The results showed that the discriminant accuracies of the re-substitution and cross-validation were 91.9% and 89.2%, respectively, while the discrimination accuracy of cross-validation of the traditional Fisher discrimination model was 86.5%. The discrimination accuracy of this model was higher than that of the traditional Fisher discrimination model. Therefore, the Fisher discriminant model for mine water inrush sources based on fuzzy cluster analysis and factor analysis established in this study can improve the accuracy of a water source discrimination model, and can provide a useful reference for mine water disaster prevention.
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References
Aryafar A, Ardejani FD (2011) R-mod factor analysis, a popular multivariate statistical technique to evaluate water quality in Khaf-Sangan Basin, Mashhad, Northeast of Iran. Arab J Geosci 6(3):893–900. https://doi.org/10.1007/s12517-011-0367-7)
Bi J, Li XJ, Chen LY (2019) R-factor fisher discrimination for rock burst hazard level prediction. China Saf Sci J 29(12):103–109. https://doi.org/10.16265/j.cnki.issn1003-3033.2019.12.017)
Bu HM, Tan X, Li SY, Zhang QF (2010) Water quality assessment of the Jinshui River (China) using multivariate statistical techniques. Environ Earth Sci 60(8):1631–1639. https://doi.org/10.1007/s12665-009-0297-9)
Fan L, Wu NN, Huo QG, Wang MM (2008) Pattern recognition analysis of 5 kinds of vegetable oil and fat by the content of fatty acids. Chin J Anal Chem 36(08):1133–1137. https://doi.org/10.3321/j.issn:0253-3820.2008.08.026
Fazelabdolabadi B, Golestan MH (2020) Towards Bayesian quantification of permeability in micro-scale porous structures-the database of micro networks. HighTech Innov J 1(4):148–160. https://doi.org/10.28991/HIJ-2020-01-04-02)
Gong L, Jin CL (2009) Fuzzy comprehensive evaluation for carrying capacity of regional water resources. Water Resour Manag 23(12):2505–2513. https://doi.org/10.1007/s11269-008-9393-y)
Guiamel IA, Lee HS (2020) Watershed modelling of the mindanao river basin in the philippines using the SWAT for water resource management. Civ Eng J 6(4):626–648. https://doi.org/10.28991/cej-2020-03091496)
Harisuseno D (2020) Meteorological drought and its relationship with southern oscillation index. Civ Eng J (SOI) 6(10):1864–1875. https://doi.org/10.28991/cej-2020-03091588
Huang PH, Wang XY (2018) Piper-PCA-Fisher recognition model of water inrush source: a case study of the Jiaozuo mining area. Geofuids 1:1–10. https://doi.org/10.1155/2018/9205025)
Javadinejad S, Dara R, Jafary F (2020) Climate change scenarios and effects on snow-melt runoff. Civ Eng J 6(9):1715–1725. https://doi.org/10.28991/cej-2020-03091577)
Jiang XY, Cheng CQ (2009) Hydrochemical classification and identification of groundwater in mining region using multivariate statistical analysis. Hydrol Eng Geol 36(04):16–20. https://doi.org/10.3969/j.issn.1000-3665.2009.04.005
Jiang CL, Jiang ZQ, Sun Q (2012) Classification of rocks surrounding tunnel using Fisher discriminant analysis method. J China Coal Soc 37(10):1665–1670. https://doi.org/10.13225/j.cnki.jccs.2012.10.008
Keskin TE, Düğenci M, Kaçaroğlu F (2015) Prediction of water pollution sources using artifcial neural networks in the study areas of Sivas, Karabük and Bartın (Turkey). Environ Earth Sci 73(9):5333–5347. https://doi.org/10.1007/s12665-014-3784-6)
Li B, Wu Q, Liu Z (2020) Identification of mine water inrush source based on PCA-FDA: Xiandewang Coal Mine case. Geofluids 2:1–8. https://doi.org/10.1155/2020/2584094)
Liu X, Chen LW, Lin ML, Li SD (2013) Fisher discriminant analysis for coal mining inrush water source under mining-induced disturbance and inversion of groundwater recharge relation. Hydrol Eng Geol 40(04):36–43. https://doi.org/10.16030/j.cnki.issn.1000-3665.2013.04.021)
Liu XD, Wang XC, Pedrycz W (2015) Fuzzy clustering with semantic interpretation. Appl Soft Comput 26:21–30. https://doi.org/10.1016/j.asoc.2014.09.037
Ma R, Shi JS, Liu JC, Gui CL (2014) Combined use of multivariate statistical analysis and hydrochemical analysis for groundwater quality evolution. J Earth Sci China 25(3):587–597. https://doi.org/10.1007/s12583-014-0446-2
Qian JZ, Tong Y, Ma L, Zhao WD, Zhang RD, He XR (2010) Hydrochemical characteristics and groundwater source identification of a multiple aquifer system in a coal mine. Mine Water Environ 37(3):528–540. https://doi.org/10.1007/s10230-017-0493-x)
Shrestha S, Kazama F (2007) Assessment of surface water quality using multivariate statistical techniques: a case study of the Fuji river basin Japan. Environ Modell Softw 22(4):464–475. https://doi.org/10.1016/j.envsoft.2006.02.001
Sugiyama M (2007) Dimensionality reduction of multimodal labeled data by local fisher discriminant analysis. J Mach Learn Res 8:1027–1061. https://doi.org/10.1007/s10846-007-9130-4
Sun LH (2014) Statistical analysis of hydrochemistry of groundwater and its implications for water source identification: a case study. Arab J Geosci 7(9):3417–3425. https://doi.org/10.1007/s12517-013-1061-8
Sun LH, Gui HR (2013) Statistical analysis of deep groundwater geochemistry from Taoyuan Coal Mine, northern Anhui Province. J China Coal Soc 38(S2):442–447. https://doi.org/10.13225/j.cnki.jccs.2013.s2.006
Tallini M, Falcone RA, Carucci V, Falgiani A, Parisse B, Petitta M (2014) Isotope hydrology and geochemical modeling: new insights into the recharge processes and water-rock interactions of a fissured carbonate aquifer (Gran Sasso, Central Italy). Environ Earth Sci 72(12):4957–4971. https://doi.org/10.1007/s12665-014-3364-9
Wang DD, Shi LQ (2019) Source identification of mine water inrush:a discussion on the application of hydrochemical method. Arab J Geosci 12(2):58. https://doi.org/10.1007/s12517-018-4076-3
Wang XY, Xu T, Huang D (2011) Application of distance discriminance in identifying water inrush resource in similar coalmine. J China Coal Soc 36(08):1354–1358. https://doi.org/10.13225/j.cnki.jccs.2011.08.028
Wang Y, Zhou MR, Yan PC, He CY, Liu D (2017) Identifcation of coalmine water inrush source with PCA-BP model based on laser-induced fuorescence technology. Spectrosc Spectr Anal 37(3):978–983. https://doi.org/10.3964/j.issn.1000-0593(2017)03-0978-06
Wang TT, Jin DW, Liu J, Yang J, Wang XY, Zhao W (2019) Application of dynamic weight-set pair analysis model in mine water inrush discrimination. J China Coal Soc 44(9):2840–2850. https://doi.org/10.13225/j.cnki.jccs.2018.1419
Wei WX, Lu XM, Shi LQ (2010) Identification method of multi-water source of mine water inrush. J China Coal Soc 35(05):811–815. https://doi.org/10.13225/j.cnki.jccs.2010.05.027
Wu XT, Wang DC, Fang XZ (2008) Evaluation of agricultural equipment level based on factor analysis. Trans Chin Soc Agric Mach 39(010):100–104. https://doi.org/10.3901/JME.2008.09.177
Wu J, Li P, Qian H, Duan Z, Zhang X (2014) Using correlation and multivariate statistical analysis to identify hydrogeochemical processes affecting the major ion chemistry of waters: case study in Laoheba phosphorite mine in Sichuan China. Arab J Geosci 7(10):3973–3982. https://doi.org/10.1007/s12517-013-1057-4
Wu Q, Guo XM, Shen JJ, Xu S, Liu SQ, Zeng YF (2016) Risk assessment of water inrush from aquifers underlying the Gushuyuan coal mine China. Mine Water Environ 36(1):1–8. https://doi.org/10.1007/s10230-016-0410-8)
Wu Q, Mu WP, Xing Y, Qian C, Shen JJ, Wang Y, Zhao DK (2019) Source discrimination of mine water inrush using multiple methods: a case study from the Beiyangzhuang Mine Northern China. B Eng Geol Environ 78(1):469–482. https://doi.org/10.1007/s10064-017-1194-1
Xu ZJ, Zheng JJ, Zhang J, Ma Q (2010) Application of cluster analysis and factor analysis to evaluation of loess collapsibility. Rock Soil Mech 31(S2):407–411. https://doi.org/10.16285/j.rsm.2010.s2.041
Xu B, Zhang Y, Jiang L (2012) Coupled model based on grey relational analysis and stepwise discriminant analysis for water source identification of mine water inrush. Rock Soil Mech 33(10):3122–3128. https://doi.org/10.1007/s11783-011-0280-z
Xu X, Li YZ, Tian KY, Zhang RL (2018) Application of ACPSO-BP neural network in discriminating mine water inrush source. J Chongqing Univ 41(06):91–101
Xu YY, Li W, Wang ZL, Lu HS, Fan Y, Wang B, Wang YN (2020) Simulation of relationship between evaporation and meteorological elements of bare ground diving based on principal component analysis. J China Hydrol 40(04):7–1339. https://doi.org/10.19797/j.cnki.1000-0852.20200019
Yan WF, Xia XH, Pan BL, Gu CS, Yue JG (2016) The fuzzy comprehensive evaluation of water and sand inrush risk during underground mining. J Intell Fuzzy Syst 30(4):2289–2295. https://doi.org/10.3233/IFS-151998
Yan BQ, Ren FH, Cai MF, Qiao C (2020) Bayesian model based on Markov chain Monte Carlo for identifying mine water sources in Submarine Gold Mining. J Clean Prod 2020:253. https://doi.org/10.1016/j.jclepro.2020.120008
Yang YG, Huang FC (2007) Water source determination of mine inflow based on non-linear method. J China Univ Min Technol 36(03):283–286. https://doi.org/10.3321/j.issn:1000-1964.2007.03.002
Zhang H, Yao DX (2020) The Bayes recognition model for mine water inrush source based on multiple logistic regression analysis. Mine Water Environ. https://doi.org/10.1007/s10230-020-00699-2
Zhang Y, Guo F, Meng W, Wang XQ (2009) Water quality assessment and source identification of Daliao River basin using multivariate statistical methods. Environ Monit Assess 152:105–121. https://doi.org/10.1007/s10661-008-0300-z
Zhang H, Xing HF, Yao DX, Liu LL, Xue DR, Guo F (2019a) The multiple logistic regression recognition model for mine water inrush source based on cluster analysis. Environ Earth Sci 78(20):612.1-612.15. https://doi.org/10.1007/s12665-019-8624-2
Zhang HT, Xu GQ, Chen XQ, Wei J, Yu ST, Yang TT (2019b) Hydrogeochemical characteristics and groundwater inrush source identification for a multi-aquifer system in a coal mine. Acta Geol Sin-Engl 93(6):1922–1932. https://doi.org/10.1111/1755-6724.14299
Zhang B, Min H, Liu S, An YR, Li C, Zhu ZX (2020) X-Ray fluorescence spectroscopy combined with discriminant analysis to identify imported iron ore origin and brand. Spectrosc Spectr Anal 40(08):2640–2646. https://doi.org/10.3964/j.issn.1000-0593(2020)08-2640-07
Zhou J, Shi XZ, Wang HY (2010) Water-bursting source determination of mine based on discriminant analysis model. J China Coal Soc 35(2):278–282. https://doi.org/10.13225/j.cnki.jccs.2010.02.025
Žibret G, Šajn R (2010) Hunting for geochemical associations of elements: factor analysis and self-organising maps. Math Geosci 42(6):681–703. https://doi.org/10.1007/s11004-010-9288-3
Acknowledgements
The study was supported by the National Natural Science Foundation of China (Grant No. 41272278) and the Scientific Research Platform Innovation Team Construction Project in Universities of Anhui (Grant No. 2016-2018-24). The authors would also like to thank Dr. Ju for his valuable comments and suggestions for improvement of the manuscript.
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Bi, Y., Wu, J., Zhai, X. et al. Discriminant analysis of mine water inrush sources with multi-aquifer based on multivariate statistical analysis. Environ Earth Sci 80, 144 (2021). https://doi.org/10.1007/s12665-021-09450-8
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DOI: https://doi.org/10.1007/s12665-021-09450-8