A Comparison of Inversion Methods for Surrogate‐Based Groundwater Contamination Source Identification With Varying Degrees of Model Complexity
Abstract Accurate identification of groundwater contamination sources is important for designing efficacious site remediation strategies. Currently, the methods for identifying contamination sources mainly fall into three distinct categories: simulation optimization, Bayesian inference, and data ass...
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| Format: | Article |
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Wiley
2024-04-01
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| Series: | Water Resources Research |
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| Online Access: | https://doi.org/10.1029/2023WR036051 |
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| author | Zhenbo Chang Zhilin Guo Kewei Chen Zibo Wang Yang Zhan Wenxi Lu Chunmiao Zheng |
| author_facet | Zhenbo Chang Zhilin Guo Kewei Chen Zibo Wang Yang Zhan Wenxi Lu Chunmiao Zheng |
| author_sort | Zhenbo Chang |
| collection | DOAJ |
| description | Abstract Accurate identification of groundwater contamination sources is important for designing efficacious site remediation strategies. Currently, the methods for identifying contamination sources mainly fall into three distinct categories: simulation optimization, Bayesian inference, and data assimilation. Each method has its own advantages and disadvantages under specific site conditions. To evaluate the applicability of these methods, we chose one representative inversion algorithm from each category, namely the Improved Butterfly Optimization Algorithm (IBOA) for simulation optimization, the Ensemble Smoother with Multiple Data Assimilation (ES‐MDA) for data assimilation, and the DiffeRential Evolution Adaptive Metropolis with a Snooker Update and Sampling from a Past Archive (DREAM(ZS)) for Bayesian inference. We conducted a comprehensive evaluation of these methods' performance under different model complexities, employing a surrogate model as a substitute for the complex forward model. By addressing two distinct problems involving conservative pollutant transport and Light Non‐Aqueous Phase Liquid (LNAPL) transport with biodegradation, we employed four criteria (elapsed time, result accuracy, posterior probability distribution, and noise resistance) for evaluation. The findings unequivocally indicate that DREAM(ZS) outperforms others in terms of result accuracy and posterior probability distribution. It also adeptly navigates the interrelations among disparate unknown variables. The strength of ES‐MDA lies in its efficiency. It achieves relatively satisfactory results with a reduced computational burden. In contrast, IBOA underperforms in both test problems. In terms of resistance to noise, both DREAM(ZS) and ES‐MDA perform better than IBOA does. |
| format | Article |
| id | doaj-art-7fad5f3aaec546afa1e092c9e5f9a8ab |
| institution | OA Journals |
| issn | 0043-1397 1944-7973 |
| language | English |
| publishDate | 2024-04-01 |
| publisher | Wiley |
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| series | Water Resources Research |
| spelling | doaj-art-7fad5f3aaec546afa1e092c9e5f9a8ab2025-08-20T02:09:24ZengWileyWater Resources Research0043-13971944-79732024-04-01604n/an/a10.1029/2023WR036051A Comparison of Inversion Methods for Surrogate‐Based Groundwater Contamination Source Identification With Varying Degrees of Model ComplexityZhenbo Chang0Zhilin Guo1Kewei Chen2Zibo Wang3Yang Zhan4Wenxi Lu5Chunmiao Zheng6State Environmental Protection Key Laboratory of Integrated Surface Water‐Groundwater Pollution Control School of Environmental Science & Engineering Southern University of Science and Technology Shenzhen ChinaState Environmental Protection Key Laboratory of Integrated Surface Water‐Groundwater Pollution Control School of Environmental Science & Engineering Southern University of Science and Technology Shenzhen ChinaState Environmental Protection Key Laboratory of Integrated Surface Water‐Groundwater Pollution Control School of Environmental Science & Engineering Southern University of Science and Technology Shenzhen ChinaCollege of New Energy and Environment Jilin University Changchun ChinaState Environmental Protection Key Laboratory of Integrated Surface Water‐Groundwater Pollution Control School of Environmental Science & Engineering Southern University of Science and Technology Shenzhen ChinaCollege of New Energy and Environment Jilin University Changchun ChinaState Environmental Protection Key Laboratory of Integrated Surface Water‐Groundwater Pollution Control School of Environmental Science & Engineering Southern University of Science and Technology Shenzhen ChinaAbstract Accurate identification of groundwater contamination sources is important for designing efficacious site remediation strategies. Currently, the methods for identifying contamination sources mainly fall into three distinct categories: simulation optimization, Bayesian inference, and data assimilation. Each method has its own advantages and disadvantages under specific site conditions. To evaluate the applicability of these methods, we chose one representative inversion algorithm from each category, namely the Improved Butterfly Optimization Algorithm (IBOA) for simulation optimization, the Ensemble Smoother with Multiple Data Assimilation (ES‐MDA) for data assimilation, and the DiffeRential Evolution Adaptive Metropolis with a Snooker Update and Sampling from a Past Archive (DREAM(ZS)) for Bayesian inference. We conducted a comprehensive evaluation of these methods' performance under different model complexities, employing a surrogate model as a substitute for the complex forward model. By addressing two distinct problems involving conservative pollutant transport and Light Non‐Aqueous Phase Liquid (LNAPL) transport with biodegradation, we employed four criteria (elapsed time, result accuracy, posterior probability distribution, and noise resistance) for evaluation. The findings unequivocally indicate that DREAM(ZS) outperforms others in terms of result accuracy and posterior probability distribution. It also adeptly navigates the interrelations among disparate unknown variables. The strength of ES‐MDA lies in its efficiency. It achieves relatively satisfactory results with a reduced computational burden. In contrast, IBOA underperforms in both test problems. In terms of resistance to noise, both DREAM(ZS) and ES‐MDA perform better than IBOA does.https://doi.org/10.1029/2023WR036051simulation optimizationBayesian inferencesurrogate modeldata assimilationgroundwater contamination source identificationhyperparameter optimization |
| spellingShingle | Zhenbo Chang Zhilin Guo Kewei Chen Zibo Wang Yang Zhan Wenxi Lu Chunmiao Zheng A Comparison of Inversion Methods for Surrogate‐Based Groundwater Contamination Source Identification With Varying Degrees of Model Complexity Water Resources Research simulation optimization Bayesian inference surrogate model data assimilation groundwater contamination source identification hyperparameter optimization |
| title | A Comparison of Inversion Methods for Surrogate‐Based Groundwater Contamination Source Identification With Varying Degrees of Model Complexity |
| title_full | A Comparison of Inversion Methods for Surrogate‐Based Groundwater Contamination Source Identification With Varying Degrees of Model Complexity |
| title_fullStr | A Comparison of Inversion Methods for Surrogate‐Based Groundwater Contamination Source Identification With Varying Degrees of Model Complexity |
| title_full_unstemmed | A Comparison of Inversion Methods for Surrogate‐Based Groundwater Contamination Source Identification With Varying Degrees of Model Complexity |
| title_short | A Comparison of Inversion Methods for Surrogate‐Based Groundwater Contamination Source Identification With Varying Degrees of Model Complexity |
| title_sort | comparison of inversion methods for surrogate based groundwater contamination source identification with varying degrees of model complexity |
| topic | simulation optimization Bayesian inference surrogate model data assimilation groundwater contamination source identification hyperparameter optimization |
| url | https://doi.org/10.1029/2023WR036051 |
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