Aquifer hydraulic conductivity prediction via coupling model of MCMC-ANN

Chun-lei GUI; Zhen-xing WANG; Rong MA; Xue-feng ZUO

doi:10.19637/j.cnki.2305-7068.2021.01.001

Volume 9 Issue 1

Mar. 2021

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Chun-lei GUI, Zhen-xing WANG, Rong MA, 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

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Chun-lei GUI, Zhen-xing WANG, Rong MA, 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

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Aquifer hydraulic conductivity prediction via coupling model of MCMC-ANN

doi: 10.19637/j.cnki.2305-7068.2021.01.001

The Institute of Hydrogeology and Environmental Geology, Key Laboratory of Groundwater Sciences and Engineering, MNR, Shijiazhuang 050061, China

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Corresponding author: WANG Zhen-xing, E-mail: wangzhxing9987@126.com
Received Date: 2020-07-15
Accepted Date: 2020-09-12
Publish Date: 2021-03-28

Abstract

Abstract

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.
- Grain-size distribution,
- Hydraulic conductivity,
- ANN,
- GLUE,
- MCMC,
- Stochastic Observation Error (SOE)

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References(31)

References

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