Simultaneous identification of groundwater contamination source information, model parameters, and boundary conditions under an unknown boundary mode
Author:
Funder
Graduate Innovation Fund of Jilin University
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s00477-024-02795-z.pdf
Reference46 articles.
1. Anshuman A, Eldho TI (2022) Entity aware sequence to sequence learning using LSTMs for estimation of groundwater contamination release history and transport parameters. J Hydrol 608:127662. https://doi.org/10.1016/j.jhydrol.2022.127662
2. Bai Y, Lu W, Li J, Chang Z, Wang H (2022) Groundwater contamination source identification using improved differential evolution Markov chain algorithm. Environ Sci Pollut Res 29(13):19679–19692. https://doi.org/10.1007/s11356-021-17120-2
3. Bian J, Ruan D, Wang Y, Sun X, Gu Z (2023) Bayesian ensemble machine learning-assisted deterministic and stochastic groundwater DNAPL source inversion with a homotopy-based progressive search mechanism. J Hydrol 624:129925. https://doi.org/10.1016/j.jhydrol.2023.129925
4. Chang Z, Lu W, Wang Z (2021a) A differential evolutionary Markov chain algorithm with ensemble smoother initial point selection for the identification of groundwater contaminant sources. J Hydrol 603:126918. https://doi.org/10.1016/j.jhydrol.2021.126918
5. Chang Z, Lu W, Wang H, Li J, Luo J (2021b) Simultaneous identification of groundwater contaminant sources and simulation of model parameters based on an improved singlecomponent adaptive Metropolis algorithm. Hydrogeol J 29(2):859–873. https://doi.org/10.1007/s10040-020-02257-0
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