Groundwater contamination source identification using improved differential evolution Markov chain algorithm

• Published in: Environmental science and pollution research international Vol. 29; no. 13; pp. 19679 - 19692
• Main Authors: Bai, Yukun, Lu, Wenxi, Li, Jiuhui, Chang, Zhengbo, Wang, Han
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2022; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Aquatic Pollution; Atmospheric Protection/Air Quality Control/Air Pollution; Bayes Theorem; Bayesian analysis; Bayesian theory; Computer applications; Computer simulation; Contamination; Earth and Environmental Science; Ecotoxicology; Environment; Environmental Chemistry; Environmental Health; Environmental science; Evolution; Evolutionary algorithms; Evolutionary computation; Groundwater; groundwater contamination; Groundwater pollution; hybrids; Iterative methods; kriging; Markov analysis; Markov chain; Markov Chains; Mathematical models; Monte Carlo Method; Monte Carlo simulation; Mutation; Parameter identification; Pollution sources; remediation; Research Article; simulation models; Waste Water Technology; Water Management; Water Pollution - analysis; Water Pollution Control; Groundwater contamination; Differential evolution; Kriging surrogate model; Bayesian theory; Kent mapping chaotic sequence; Hybrid mutation
• ISSN: 0944-1344; 1614-7499; 1614-7499
• DOI: 10.1007/s11356-021-17120-2

The groundwater contamination source identification (GCSI) can provide important bases for the design of pollution remediation plans. The Bayesian theory is commonly used in the GCSI problem. Usually, we use the Markov chain Monte Carlo (MCMC) method to realize the Bayesian framework. However, due to the ill-posed nature of the GCSI and the system model’s complexity, the conventional MCMC algorithm is time-consuming and has low accuracy. In this study, we proposed an adaptive mutation differential evolution Markov chain (AM-DEMC) algorithm. In this algorithm, the Kent mapping chaotic sequence method, combined with differential evolution (DE) algorithm, was used to generate the initial population. In the iteration process, we introduced a hybrid mutation strategy to generate the candidate vectors. Moreover, we adaptively adjust the essential parameter F of the AM-DEMC algorithm according to the individual fitness value. For further improving the efficiency of solving the GCSI problem, the Kriging method was used to establish a surrogate model to avoid the enormous computational load associated with the numerical simulation model. Finally, a hypothetical groundwater contamination case was given to verify the effectiveness of the AM-DEMC algorithm. The results indicated that the proposed AM-DEMC algorithm successfully identified the contamination sources’ characteristics and simulation model’s parameters. It also exhibited stronger search-ability and higher accuracy than the MCMC and DE-MC algorithms.