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Groundwater contamination source identification using improved differential evolution Markov chain algorithm.

Yukun Bai1,2,3, Wenxi Lu4,5,6, Jiuhui Li1,2,3

  • 1Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun, 130021, China.

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|October 31, 2021
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Summary

A new adaptive mutation differential evolution Markov chain (AM-DEMC) algorithm improves groundwater contamination source identification. This method offers higher accuracy and efficiency than conventional algorithms for pollution remediation planning.

Keywords:
Bayesian theoryDifferential evolutionGroundwater contaminationHybrid mutationKent mapping chaotic sequenceKriging surrogate model

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Area of Science:

  • Environmental Science
  • Geosciences
  • Computational Science

Background:

  • Groundwater contamination source identification (GCSI) is crucial for effective pollution remediation.
  • Bayesian theory and Markov chain Monte Carlo (MCMC) are standard GCSI methods.
  • Conventional MCMC algorithms are often slow and inaccurate due to GCSI's ill-posed nature and complex models.

Purpose of the Study:

  • To develop a more efficient and accurate algorithm for GCSI.
  • To address the limitations of conventional MCMC methods in complex environmental modeling.
  • To enhance the reliability of pollution remediation planning through improved source identification.

Main Methods:

  • Proposed an adaptive mutation differential evolution Markov chain (AM-DEMC) algorithm.
  • Utilized Kent mapping chaotic sequences and differential evolution (DE) for initial population generation.
  • Implemented a hybrid mutation strategy and adaptive parameter adjustment (F) for enhanced search.
  • Employed the Kriging method to create a surrogate model, reducing computational load.

Main Results:

  • The AM-DEMC algorithm successfully identified groundwater contamination source characteristics and model parameters.
  • Demonstrated superior searchability and accuracy compared to standard MCMC and DE-MC algorithms.
  • Validated effectiveness through a hypothetical groundwater contamination case study.

Conclusions:

  • The AM-DEMC algorithm provides a robust and efficient solution for GCSI.
  • This advanced method enhances the basis for designing effective groundwater pollution remediation plans.
  • The integration of surrogate modeling significantly improves computational efficiency for complex environmental problems.