Comparative study of different proposal distribution MCMC algorithms in groundwater pollution source identification
LI Xue-li1,2, LUO Jian-nan1,2, LIU Yong1,2
1. Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun 130021, China; 2. College of New Energy and Environment, Jilin University, Changchun 130021, China
Abstract：In order to obtain the optimal proposal distribution by exploring the influences of normal distribution, uniform distribution and random walk of the MCMC algorithms on the inversion identification results of groundwater pollution source, this paper established a simulation model of pollutant transport and a surrogate model using Kriging method with a hypothetical case, and the MCMC algorithm based on normal distribution, uniform distribution and random walk was developed to identify the release history of groundwater pollution sources. The results show that the inversion algorithm with uniform distribution as the proposal distribution has the advantages of high inversion accuracy, good stability and fast convergence speed, which proves to be the most suitable proposal distribution for groundwater pollution sources inversion.
李雪利, 罗建男, 刘勇. 不同建议分布MCMC算法在地下水污染源反演识别中的对比研究[J]. 中国环境科学, 2023, 43(4): 1646-1654.
LI Xue-li, LUO Jian-nan, LIU Yong. Comparative study of different proposal distribution MCMC algorithms in groundwater pollution source identification. CHINA ENVIRONMENTAL SCIENCECE, 2023, 43(4): 1646-1654.
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