Bidirectional machine learning–assisted sensitivity-based stochastic searching approach for groundwater DNAPL source characterization-Reference-Cited by-同舟云学术

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Bidirectional machine learning–assisted sensitivity-based stochastic searching approach for groundwater DNAPL source characterization

Published:2024-04-29 Issue:23 Volume:31 Page:33591-33609
ISSN:1614-7499
Container-title:Environmental Science and Pollution Research
language:en
Short-container-title:Environ Sci Pollut Res

Author:

Hou Zeyu,Lin Yingzi,Liu Tongzhe,Lu Wenxi

Funder

National Natural Science Foundation of China

Natural Science Foundation of Jilin Province

Science and Technology Research Project of Jilin Provincial Education Department

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11356-024-33405-8.pdf

Reference62 articles.

1. An Y, Lu W, Cheng W (2015) Surrogate model application to the identification of optimal groundwater exploitation scheme based on regression kriging method-a case study of Western Jilin province. Int J Environ Res Public Health 12:8897–8918

2. Ayub R, Messier KP, Serre ML (2019) Non-point source evaluation of groundwater nitrate contamination from agriculture under geologic uncertainty. Stoch Env Res Risk Assess 33(4–6):939–956

3. Azedou A, Amine A, Kisekka I et al (2023) Genetic algorithm optimization of ensemble learning approach for accurate land cover and land use mapping. Available at SSRN. https://doi.org/10.2139/ssrn.4660439

4. Barati Moghaddam M, Mazaheri M, Samani MVJ (2021) Inverse modeling of contaminant transport for pollution source identification in surface and groundwaters: a review. Groundw Sustain Dev 15:100651

5. Bian JM, Ruan DM, Wang Y et al (2023) Bayesian ensemble machine learning-assisted deterministic and stochastic groundwater DNAPL source inversion with a homotopy-based progressive search mechanism. J Hydrol 624:129925

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