Improved MCMC method based on latin hypercube sampling for inverse problems of underground water pollution

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Improved MCMC method based on latin hypercube sampling for inverse problems of underground water pollution

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[1]Zhang Shuangsheng,strong static,Liu Han Lake,et al.Improved MCMC method based on latin hypercube sampling for inverse problems of underground water pollution[J].Journal of Zhengzhou University (Engineering Science),2020,41(03):72-78.[doi:10.13705/j.issn.1671-6833.2019.02.016]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 41 Number of periods: 2020 03 Page number: 72-78 Column: Public date: 2020-07-29

Title:: Improved MCMC method based on latin hypercube sampling for inverse problems of underground water pollution

Author(s):: Zhang Shuangsheng1; 2; strong static 3; Liu Han Lake 1; Liu Xikun 2; Sun Shaohua 4; 1. School of Environment and Surveying, China University of Mining and Technology; 2. Xuzhou Urban Water Resources Management Office; 3. School of Mathematics, China University of Mining and Technology; 4. Shandong Provincial Water Supply and Drainage Water Quality Monitoring Center

Keywords:: 2D water quality model; Bayesian-Markov chain Monte Carlo method; Latin hypercube sampling; Delay rejection adaptive Metropolis algorithm; pollution source identification

CLC:: -

DOI:: 10.13705/j.issn.1671-6833.2019.02.016

Abstract:: Aiming at the problem of the result affected by samples’ initial values with classical MCMC method, when the inverse problems of underground water pollution were solved by Bayesian statistical methods, an improved MCMC method based on latin hypercube sampling was presented. An underground water pollution source identification model was built by coupling Bayesian statistical methods to two-dimensional water quality convection-diffusion equation. An example of a pollutant in the underground aquifer discharged instantly was put forward, and the pollution source information including source’’s position, intensity and discharging time was solved by classical MCMC method and improved MCMC method separately. The example showed that the inversion results affected by initial values with classical MCMC method were locally optimal or difficult to convergence; on the contrary, the improved MCMC method could achieve a global optimization. The computed values solved by the improved MCMC method were basically the same as the truth values. The accuracy and stability were improved significantly.

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Last Update: 2020-07-29