An Adaptive Surrogate-Assisted Simulation-Optimization Method for Identifying Release History of Groundwater Contaminant Sources
The simulation-optimization method, integrating the numerical model and the evolutionary algorithm, is increasingly popular for identifying the release history of groundwater contaminant sources. However, due to the usage of computationally intensive evolutionary algorithms, traditional simulation-o...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-05-01
|
| Series: | Water |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4441/14/10/1659 |
| _version_ | 1797494489899597824 |
|---|---|
| author | Mengtian Wu Jin Xu Pengjie Hu Qianyi Lu Pengcheng Xu Han Chen Lingling Wang |
| author_facet | Mengtian Wu Jin Xu Pengjie Hu Qianyi Lu Pengcheng Xu Han Chen Lingling Wang |
| author_sort | Mengtian Wu |
| collection | DOAJ |
| description | The simulation-optimization method, integrating the numerical model and the evolutionary algorithm, is increasingly popular for identifying the release history of groundwater contaminant sources. However, due to the usage of computationally intensive evolutionary algorithms, traditional simulation-optimization methods always require thousands of simulations to find appropriate solutions. Such methods yield a prohibitive computational burden if the simulation involved is time-consuming. To reduce general computation, this study proposes a novel simulation-optimization method for solving the inverse contaminant source identification problems, which uses surrogate models to approximate the numerical model. Unlike many existing surrogate-assisted methods using the pre-determined surrogate model, this paper presents an adaptive surrogate technique to construct the most appropriate surrogate model for the current numerical model. Two representative cases about identifying the release history of contaminant sources are used to investigate the accuracy and robustness of the proposed method. The results indicate that the proposed adaptive surrogate-assisted method effectively identifies the release history of groundwater contaminant sources with a higher degree of accuracy and shorter computation time than traditional methods. |
| first_indexed | 2024-03-10T01:35:02Z |
| format | Article |
| id | doaj.art-f11854657b594de9bffc156ec32ef52b |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-10T01:35:02Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-f11854657b594de9bffc156ec32ef52b2023-11-23T13:35:57ZengMDPI AGWater2073-44412022-05-011410165910.3390/w14101659An Adaptive Surrogate-Assisted Simulation-Optimization Method for Identifying Release History of Groundwater Contaminant SourcesMengtian Wu0Jin Xu1Pengjie Hu2Qianyi Lu3Pengcheng Xu4Han Chen5Lingling Wang6State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing 210098, ChinaState Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing 210098, ChinaState Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing 210098, ChinaState Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing 210098, ChinaState Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing 210098, ChinaState Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing 210098, ChinaState Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing 210098, ChinaThe simulation-optimization method, integrating the numerical model and the evolutionary algorithm, is increasingly popular for identifying the release history of groundwater contaminant sources. However, due to the usage of computationally intensive evolutionary algorithms, traditional simulation-optimization methods always require thousands of simulations to find appropriate solutions. Such methods yield a prohibitive computational burden if the simulation involved is time-consuming. To reduce general computation, this study proposes a novel simulation-optimization method for solving the inverse contaminant source identification problems, which uses surrogate models to approximate the numerical model. Unlike many existing surrogate-assisted methods using the pre-determined surrogate model, this paper presents an adaptive surrogate technique to construct the most appropriate surrogate model for the current numerical model. Two representative cases about identifying the release history of contaminant sources are used to investigate the accuracy and robustness of the proposed method. The results indicate that the proposed adaptive surrogate-assisted method effectively identifies the release history of groundwater contaminant sources with a higher degree of accuracy and shorter computation time than traditional methods.https://www.mdpi.com/2073-4441/14/10/1659simulation-optimization methodsurrogate modelinginverse contaminant source identification problems |
| spellingShingle | Mengtian Wu Jin Xu Pengjie Hu Qianyi Lu Pengcheng Xu Han Chen Lingling Wang An Adaptive Surrogate-Assisted Simulation-Optimization Method for Identifying Release History of Groundwater Contaminant Sources Water simulation-optimization method surrogate modeling inverse contaminant source identification problems |
| title | An Adaptive Surrogate-Assisted Simulation-Optimization Method for Identifying Release History of Groundwater Contaminant Sources |
| title_full | An Adaptive Surrogate-Assisted Simulation-Optimization Method for Identifying Release History of Groundwater Contaminant Sources |
| title_fullStr | An Adaptive Surrogate-Assisted Simulation-Optimization Method for Identifying Release History of Groundwater Contaminant Sources |
| title_full_unstemmed | An Adaptive Surrogate-Assisted Simulation-Optimization Method for Identifying Release History of Groundwater Contaminant Sources |
| title_short | An Adaptive Surrogate-Assisted Simulation-Optimization Method for Identifying Release History of Groundwater Contaminant Sources |
| title_sort | adaptive surrogate assisted simulation optimization method for identifying release history of groundwater contaminant sources |
| topic | simulation-optimization method surrogate modeling inverse contaminant source identification problems |
| url | https://www.mdpi.com/2073-4441/14/10/1659 |
| work_keys_str_mv | AT mengtianwu anadaptivesurrogateassistedsimulationoptimizationmethodforidentifyingreleasehistoryofgroundwatercontaminantsources AT jinxu anadaptivesurrogateassistedsimulationoptimizationmethodforidentifyingreleasehistoryofgroundwatercontaminantsources AT pengjiehu anadaptivesurrogateassistedsimulationoptimizationmethodforidentifyingreleasehistoryofgroundwatercontaminantsources AT qianyilu anadaptivesurrogateassistedsimulationoptimizationmethodforidentifyingreleasehistoryofgroundwatercontaminantsources AT pengchengxu anadaptivesurrogateassistedsimulationoptimizationmethodforidentifyingreleasehistoryofgroundwatercontaminantsources AT hanchen anadaptivesurrogateassistedsimulationoptimizationmethodforidentifyingreleasehistoryofgroundwatercontaminantsources AT linglingwang anadaptivesurrogateassistedsimulationoptimizationmethodforidentifyingreleasehistoryofgroundwatercontaminantsources AT mengtianwu adaptivesurrogateassistedsimulationoptimizationmethodforidentifyingreleasehistoryofgroundwatercontaminantsources AT jinxu adaptivesurrogateassistedsimulationoptimizationmethodforidentifyingreleasehistoryofgroundwatercontaminantsources AT pengjiehu adaptivesurrogateassistedsimulationoptimizationmethodforidentifyingreleasehistoryofgroundwatercontaminantsources AT qianyilu adaptivesurrogateassistedsimulationoptimizationmethodforidentifyingreleasehistoryofgroundwatercontaminantsources AT pengchengxu adaptivesurrogateassistedsimulationoptimizationmethodforidentifyingreleasehistoryofgroundwatercontaminantsources AT hanchen adaptivesurrogateassistedsimulationoptimizationmethodforidentifyingreleasehistoryofgroundwatercontaminantsources AT linglingwang adaptivesurrogateassistedsimulationoptimizationmethodforidentifyingreleasehistoryofgroundwatercontaminantsources |