• ISSN 2305-7068
  • Indexed by ESCI CABI CAS
  • Scopus GeoRef AJ CNKI
Advanced Search
Volume 9 Issue 1
Mar.  2021
Turn off MathJax
Article Contents
GUI Chun-lei, WANG Zhen-xing, MA Rong, et al. 2021: Aquifer hydraulic conductivity prediction via coupling model of MCMC-ANN. Journal of Groundwater Science and Engineering, 9(1): 1-11. doi: 10.19637/j.cnki.2305-7068.2021.01.001
Citation: GUI Chun-lei, WANG Zhen-xing, MA Rong, et al. 2021: Aquifer hydraulic conductivity prediction via coupling model of MCMC-ANN. Journal of Groundwater Science and Engineering, 9(1): 1-11. doi: 10.19637/j.cnki.2305-7068.2021.01.001

Aquifer hydraulic conductivity prediction via coupling model of MCMC-ANN

doi: 10.19637/j.cnki.2305-7068.2021.01.001
More Information
  • Corresponding author: WANG Zhen-xing, E-mail: wangzhxing9987@126.com
  • Received Date: 2020-07-15
  • Accepted Date: 2020-09-12
  • Publish Date: 2021-03-15
  • Grain-size distribution data, as a substitute for measuring hydraulic conductivity (K), has often been used to get K value indirectly. With grain-size distribution data of 150 sets of samples being input data, this study combined the Artificial Neural Network technology (ANN) and Markov Chain Monte Carlo method (MCMC), which replaced the Monte Carlo method (MC) of Generalized Likelihood Uncertainty Estimation (GLUE), to establish the GLUE-ANN model for hydraulic conductivity prediction and uncertainty analysis. By means of applying the GLUE-ANN model to a typical piedmont region and central region of North China Plain, and being compared with actually measured values of hydraulic conductivity, the relative error ranges are between 1.55% and 23.53% and between 14.08% and 27.22% respectively, the accuracy of which can meet the requirements of groundwater resources assessment. The global best parameter gained through posterior distribution test indicates that the GLUE-ANN model, which has satisfying sampling efficiency and optimization capability, is able to reasonably reflect the uncertainty of hydrogeological parameters. Furthermore, the influence of stochastic observation error (SOE) in grain-size analysis upon prediction of hydraulic conductivity was discussed, and it is believed that the influence can not be neglected.
  • 加载中
  • Alfaro Soto MA, Chang HK, Th M, et al. 2017. Fractal-based models for the unsaturated soil hydraulic functions. Geoderma, 306: 144-151. doi:  10.1016/j.geoderma.2017.07.019
    Awad HS, Bassam AM. 2001. A computer program to calculate hydraulic conductivity from grain size data in Saudi Arabia. International Journal of Water Resources Development, 17(2): 237-246. doi:  10.1080/07900620120031298
    Das SK, Samui P, Sabat AK. 2012. Prediction of field hydraulic conductivity of clay liners using an artificial neural network and support vector machine. International Journal of Geomechanics, 12(5): 606-611. doi:  10.1061/(ASCE)GM.1943-5622.0000129
    David CW, Asce F. 2003. Goodbye, Hazen; Hello, Kozeny-Carman. Journal of Geotechnical and Geoenvironmental Engineering, 129(11): 1054-1056. doi:  10.1061/(ASCE)1090-0241(2003)129:11(1054)
    DONG Pei. 2010. Laboratory studies of sand column on the dynamic evaporation on a shallow water table. M.S. thesis, Beijing: China University of Geosciences: 10-22. (in Chinese)
    Erzin Y, Gumaste SD, Gupta AK, et al. 2009. Artificial neural network (ANN) models for determining hydraulic conductivity of compacted fine-grained soil. Canadian Geotechnical Journal, 46: 955-968 doi:  10.1139/T09-035
    FAN Gui-sheng, XING Ri-xian, ZHANG Ming-bin. 2012. Experimental study on permeability of the sandy gravel media with different gradation. Journal of Taiyuan University of Technology, 43(3): 373-378. (in Chinese) http://www.cqvip.com/QK/90007A/20123/41765758.html
    GONG Guang-lu, QIAN Min-ping. 2003. Applied stochastic coursebook and its application to algorithm and intelligent computing. Beijing: Tsinghua University Press: 35-58. (in Chinese)
    Haario H, Saksman E, Tamminen J. 2005. Compon-entwise adaptation for high dimensional MCMC. Computational Statistics, 20(2): 265-273. doi:  10.1007/BF02789703
    Hasan M, Ozer C, Ramazan M, et al. 2006. Comparison of artificial neural and regression pedotransfer functions for prediction of soil water retention and saturated hydraulic conductivity. Soil and Tillage Research, 90(2): 108-116. http://www.sciencedirect.com/science/article/pii/S0167198705002436
    Haykin S. 2004. Neural Networks: A compre-hensive foundation, second edition. Beijing: China Machine Press: 46-55. (in Chinese)
    Isik Y, Marian M, Martin B. 2012. Neural com-puting models for prediction of permeability coefficient of coarse-grained soils. Neural Computing and Applications, 21(5): 957-968. doi:  10.1007/s00521-011-0535-4
    JI Rui-li, ZHANG Ming, SU Rui, 2016. Research of in-situ hydraulic test method by using double packer equipment. Journal of Ground-water Science and Engineering, 4(1): 41-51.
    Justine O. 2007. Evaluation of empirical formulae for determination of hydraulic conductivity based on Grain-size analysis. Journal of American Science, 3(3): 54-60. http://www.researchgate.net/publication/254793785_Evaluation_of_Empirical_Formulae_for_Determination_of_Hydraulic_Conductivity_based_on_Grain-Size_Analysis
    Keith B. 2006. A manifesto for the equifinality thesis. Journal of Hydrology, 320(1-2): 18-36. doi:  10.1016/j.jhydrol.2005.07.007
    Koekkoek JW, Booltink H. 1999. Neural network models to predict soil water retention. European Journal of Soil Science, 50(3): 489-495. doi:  10.1046/j.1365-2389.1999.00247.x
    LI Shou-ju, LIU Ying-xi, WANG Deng-gang, et al. 2002. Inversion algorithm of permeability coefficients of rockmass and its application based on artificial neural network. Chinese Journal of Rock Mechanics and Engineering, 21(4): 479-483. (in Chinese) http://www.researchgate.net/publication/297311638_Inversion_algorithm_of_permeability_coefficients_of_rockmass_and_its_application_based_on_artificial_neural_network
    LU Le, WU Ji-chun. 2010. Bayesian analysis of uncertainties in groundwater numerical simulation. Journal of Hydraulic Engineering, 41(3): 264-271. http://en.cnki.com.cn/Article_en/CJFDTOTAL-SLXB201003003.htm
    LU Le, WU Ji-chun, CHEN Jing-Ya. 2008. Identification of hydrogeological parameters based on the bayesian method. Hydrogeology and Engineering Geology, 58(5): 58-63. (in Chinese) http://en.cnki.com.cn/Article_en/CJFDTOTAL-SWDG200805017.htm
    Mahmoud S, Alyaman I, Zekai S. 1993. Deter-mination of hydraulic conductivity from complete Grain-Size Distribution Curves. Ground Water, 31(4): 551-555. doi:  10.1111/j.1745-6584.1993.tb00587.x
    MAO Shi-song. 1999. Bayesian statistics. Beijing: China Statistics Press. (in Chinese)
    Nakhaei M. 2005. Estimating the saturated hy-draulic conductivity of granular material using artificial neural network based on grain size distribution curve. Islam Repub Iran, 16(1): 55-62. http://www.researchgate.net/publication/228371456_Estimating_the_Saturated_Hydraulic_Conductivity_of_Granular_Material_Using_Artificial_Neural_Network_Based_on_Grain_Size_Distribution_Curve
    Namunu JM, Ian PK, Arulanandan K. 1989. An expression for the permeability of anisotropic granular media. International Journal for Numerical and Analytical Methods in Geo-mechanics, 13(6): 575-598. doi:  10.1002/nag.1610130602
    Park HI. 2011. Development of neural network model to estimate the permeability coe-fficient of soils. Marine Georesources & Geotechnology, 29(4): 267-278.
    Russel GS. 1989. Correlations of permeability and grain size. Ground Water, 27(5): 633-636. doi:  10.1111/j.1745-6584.1989.tb00476.x
    Salarashayeri AF, Siosemarde M. 2012. Prediction of soil hydraulic conductivity from particle-size distribution. World Academy of Science, Engineering and Technology, 61: 454-457.
    Smiles DE, Youngs EG. 1963. A Multiple-well method for determining the hydraulic con-ductivity of a saturated soil in Situ. Journal of Hydrology, 1(4): 279-287. doi:  10.1016/0022-1694(63)90019-2
    Smith AFM, Roberts GO. 1993. Bayesian com-putation via the Gibbs sampler and related Markov chain Monte Carlo Methods. Journal of Royal Statistical Society, Series B, 55: 3-24. doi:  10.1111/j.2517-6161.1993.tb01466.x
    TANG Xiao-song, ZHENG Ying-ren, DONG Cheng. 2007. The prediction of seepage co-efficient of coarse-grained soil by neurotic network. Rock and Soil Mechanics, 28: 133-136, 143. (in Chinese) http://en.cnki.com.cn/Article_en/CJFDTOTAL-YTLX2007S1028.htm
    WANG Dong, Vijay PS, ZHU Yuan-shen, et al. 2009. Stochastic observation error and uncertainty in water quality evaluation. Ad-vances in Water Resources, 32: 1526-1534. doi:  10.1016/j.advwatres.2009.07.004
    XU Peng, QIU Shu-xia, JIANG Zhou-ting, et al. 2011. Fractal analysis of Kozeny-Carman constant in the homogenous porous media. Journal of Chongqing University, 34(4): 78-82. (in Chinese)
  • [1] Boulariah O, Mikhailov PA, Longobardi A, Elizariev AN, Aksenov SG, 2021: Assessment of prediction performances of stochastic models: Monthly groundwater level prediction in Southern Italy, Journal of Groundwater Science and Engineering, 9, 161-170.  doi: 10.19637/j.cnki.2305-7068.2021.02.008
    [2] Zheng Zhao-xian, Cui Xiao-shun, Zhu Pu-cheng, Guo Si-jia, 2021: Sensitivity assessment of strontium isotope as indicator of polluted groundwater for hydraulic fracturing flowback fluids produced in the Dameigou Shale of Qaidam Basin, Journal of Groundwater Science and Engineering, 9, 93-101.  doi: 10.19637/j.cnki.2305-7068.2021.02.001
    [3] Cheng Zhong-shuang, Su Chen, Zheng Zhao-xian, Li Zhuang, Wang Li-kang, Wang En-bao, 2021: Grain size characteristics and genesis of the Muxing loess in the Muling-Xingkai Plain, Northeast China, Journal of Groundwater Science and Engineering, 9, 152-160.  doi: 10.19637/j.cnki.2305-7068.2021.02.007
    [4] SONG Hong-wei, XIA Fan, MU Hai-dong, WANG Wei-qiang, SHANG Ming-sen, 2020: Study on detecting spatial distribution availability in mine goafs by ultra-high density electrical method, Journal of Groundwater Science and Engineering, 8, 281-286.  doi: 10.19637/j.cnki.2305-7068.2020.03.008
    [5] A S El-Hames, 2020: Development of a simple method for determining the influence radius of a pumping well in steady-state condition, Journal of Groundwater Science and Engineering, 8, 97-107.  doi: 10.19637/j.cnki.2305-7068.2020.02.001
    [6] Abdulrahman Th Mohammad, Qassem H Jalut, Nadia L Abbas, 2020: Predicting groundwater level of wells in the Diyala River Basin in eastern Iraq using artificial neural network, Journal of Groundwater Science and Engineering, 8, 87-96.  doi: 10.19637/j.cnki.2305-7068.2020.01.009
    [7] NAN Tian, GUO Si-jia, 2019: Influence of borehole quantity and distribution on lithology field simulation, Journal of Groundwater Science and Engineering, 7, 295-308.  doi: DOI: 10.19637/j.cnki.2305-7068.2019.04.001
    [8] Babak Ghazi, Rasoul Daneshfaraz, Esmaeil Jeihouni, 2019: Numerical investigation of hydraulic characteristics and prediction of cavitation number in Shahid Madani Dam's Spillway, Journal of Groundwater Science and Engineering, 7, 323-332.  doi: DOI: 10.19637/j.cnki.2305-7068.2019.04.003
    [9] SOSI Benjamin, BARONGO Justus, GETABU Albert, MAOBE Samson, 2019: Electrical-hydraulic conductivity model for a weathered-fractured aquifer system of Olbanita, Lower Baringo Basin, Kenya Rift, Journal of Groundwater Science and Engineering, 7, 360-372.  doi: DOI: 10.19637/j.cnki.2305-7068.2019.04.007
    [10] ZHOU Xun, 2017: Arsenic distribution and source in groundwater of Yangtze River Delta economic region, China, Journal of Groundwater Science and Engineering, 5, 343-353.
    [11] LI Jie-biao, SU Rui, YANG Jing-zhi, ZHOU Zhi-chao, JI Rui-li, ZHANG Ming, GAO Yu-feng, 2016: Distribution characteristics of tritium in the soil in Beishan area of Gansu Province, Journal of Groundwater Science and Engineering, 4, 131-140.
    [12] JI Rui-li, ZHANG Ming, SU Rui, GUO Yong-hai, ZHOU Zhi-chao, LI Jie-biao, 2016: Research of in-situ hydraulic test method by using double packer equipment, Journal of Groundwater Science and Engineering, 4, 41-51.
    [13] NAN Tian, SHAO Jing-li, CUI Ya-li, 2016: Column test-based features analysis of clogging in artificial recharge of groundwater in Beijing, Journal of Groundwater Science and Engineering, 4, 88-95.
    [14] ZHOU Li-ling, CHENG Zhe, DUAN Lei, WANG Wen-ke, 2015: Distribution of groundwater salinity and formation mechanism of fresh groundwater in an arid desert transition zone, Journal of Groundwater Science and Engineering, 3, 268-279.
    [15] CUI Xiang-xiang, FEI Yu-hong, ZHANG Zhao-ji, LI Ya-song, 2015: Distribution and migration of lead in soil of Xiao River, Shijiazhuang, Hebei Province, Journal of Groundwater Science and Engineering, 3, 98-104.
    [16] YANG Xiang-peng, ZHANG Fa-wang, CHEN Zhen, BI Xue-li, SHI Jian, ZHOU Li-xin, YANG Chen, 2015: Compiling distribution of karst in Southern China and Southeast Asia, Journal of Groundwater Science and Engineering, 3, 280-284.
    [17] XU Guang-ming, QI Jian-feng, BI Pan, BAI Gao-feng, 2015: Distribution and evolution features of salinized soil in Hebei Plain, Journal of Groundwater Science and Engineering, 3, 21-29.
    [18] CAO Wen-geng, CHEN Nan-xiang, ZHANG Yi-long, DONG Qiu-yao, 2014: Distribution of arsenic in sediment of Hangjinhou Banner- Linhe transect in Hetao Basin, North China, Journal of Groundwater Science and Engineering, 2, 87-96.
    [19] LIU Chun-lei, YANG Hui-feng, WANG Gui-ling, 2014: Back calculation of soil hydraulic parameters based on HYDRUS-1D, Journal of Groundwater Science and Engineering, 2, 46-53.
    [20] , 2013: Structural Control on Groundwater Distribution and Flow in the South of Ningxia Hui Autonomous Region, China, Journal of Groundwater Science and Engineering, 1, 1-8.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(7)  / Tables(3)

    Article Metrics

    Article views (945) PDF downloads(72) Cited by()
    Proportional views
    Related

    Submission system is out of service now, please submit to our email: gwse-iheg@188.com, hope your understanding!

    投审稿系统正在维护中,请您提交到期刊邮箱gwse-iheg@188.com,给您带来的不便敬请谅解。

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return