JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2018, Vol. 48 ›› Issue (1): 131-136.doi: 10.6040/j.issn.1672-3961.0.2017.146

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Inverse problems of pollution source identification based on Bayesian-DE

ZHANG Shuangsheng1,2, QIANG Jing3*, LIU Xikun2, LIU Hanhu1, ZHU Xueqiang1   

  1. 1. School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, Jiangsu, China;
    2. Xuzhou City Water Resource Administrative Office, Xuzhou 221018, Jiangsu, China;
    3. School of Mathematics, China University of Mining and Technology, Xuzhou 221116, Jiangsu, China
  • Received:2017-04-01 Online:2018-02-20 Published:2017-04-01

Abstract: Aiming at the inverse problem of water pollution of river with pollutant source discharged instantaneously, a methodical model was constructed based on Bayesian statistical method and two-dimensional water quality model. The posterior probability distribution of unknown parameters including source's position, intensity and discharging time was deduced. The parameters made the posterior probability density function reach the maximum value by the idea of maximum likelihood estimate and differential evolution algorithm(DE), which were viewed as the estimates of model parameters. The example showed that three estimate parameters could reach the stable state based on Bayesian-DE after 50 iterations, and correspond with truth values after 280 iterations. Compared with Bayesian-Markov chain Monte Carlo simulation(Bayesian-MCMC), 97.5% iterations of three estimate parameters reaching the stable state could be reduced, and the mean errors were decreased by 1.69%, 2.12% and 4.03% through the use of Bayesian-DE featuring rapid convergence and high accuracy.

Key words: two-dimensional water quality model, Bayesian statistical method, sudden water pollution, Bayesian-Markov chain Monte Carlo simulation, differential evolution algorithm

CLC Number: 

  • X52
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