Groundwater contaminant source identification based on an ensemble learning search framework associated with an auto xgboost surrogate
Groundwater contaminant source identification (GCSI) is commonly accompanied by search process which tweaks the unknown contaminant source information to match the simulation model outputs with the measurements. When solving identification task, search accuracy and time cost have always been challen...
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| Published in | Environmental modelling & software : with environment data news Vol. 159; p. 105588 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.01.2023
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1364-8152 1873-6726 |
| DOI | 10.1016/j.envsoft.2022.105588 |
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| Abstract | Groundwater contaminant source identification (GCSI) is commonly accompanied by search process which tweaks the unknown contaminant source information to match the simulation model outputs with the measurements. When solving identification task, search accuracy and time cost have always been challenges that must be tackled. In the present study, a novel ensemble learning search framework associated with auto extreme gradient boosting tree (xgboost) was proposed to solve GCSI. In particular, auto xgboost was employed to reduce the calculation burden caused by repeatedly running simulation model. To promote search efficiency, boosting strategy (BOS) was employed to sequentially concatenate iterative ensemble smoother, differential evolution particle filter (DEPF), and swarm evolution algorithm. The identification results indicated that: 1. Auto xgboost could substitute a numerical simulation model with desired accuracy and expeditious running speed. 2. BOS could achieve better search accuracy, but with the sacrifice of infinitesimal calculated time cost, when compared with bagging strategy.
•Novel and easy-to-perform auto xgboost was proposed as surrogate of high-calculation-cost simulation model.•Differential evolution was introduced to improve PF search capacity.•Boosting strategy was used to integrate IES, DEPF, and SEA to promote the accuracy of source identification. |
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| AbstractList | Groundwater contaminant source identification (GCSI) is commonly accompanied by search process which tweaks the unknown contaminant source information to match the simulation model outputs with the measurements. When solving identification task, search accuracy and time cost have always been challenges that must be tackled. In the present study, a novel ensemble learning search framework associated with auto extreme gradient boosting tree (xgboost) was proposed to solve GCSI. In particular, auto xgboost was employed to reduce the calculation burden caused by repeatedly running simulation model. To promote search efficiency, boosting strategy (BOS) was employed to sequentially concatenate iterative ensemble smoother, differential evolution particle filter (DEPF), and swarm evolution algorithm. The identification results indicated that: 1. Auto xgboost could substitute a numerical simulation model with desired accuracy and expeditious running speed. 2. BOS could achieve better search accuracy, but with the sacrifice of infinitesimal calculated time cost, when compared with bagging strategy.
•Novel and easy-to-perform auto xgboost was proposed as surrogate of high-calculation-cost simulation model.•Differential evolution was introduced to improve PF search capacity.•Boosting strategy was used to integrate IES, DEPF, and SEA to promote the accuracy of source identification. Groundwater contaminant source identification (GCSI) is commonly accompanied by search process which tweaks the unknown contaminant source information to match the simulation model outputs with the measurements. When solving identification task, search accuracy and time cost have always been challenges that must be tackled. In the present study, a novel ensemble learning search framework associated with auto extreme gradient boosting tree (xgboost) was proposed to solve GCSI. In particular, auto xgboost was employed to reduce the calculation burden caused by repeatedly running simulation model. To promote search efficiency, boosting strategy (BOS) was employed to sequentially concatenate iterative ensemble smoother, differential evolution particle filter (DEPF), and swarm evolution algorithm. The identification results indicated that: 1. Auto xgboost could substitute a numerical simulation model with desired accuracy and expeditious running speed. 2. BOS could achieve better search accuracy, but with the sacrifice of infinitesimal calculated time cost, when compared with bagging strategy. |
| ArticleNumber | 105588 |
| Author | Bai, Yukun Lu, Wenxi Pan, Zidong Wang, Han |
| Author_xml | – sequence: 1 givenname: Zidong surname: Pan fullname: Pan, Zidong organization: Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun, 130021, China – sequence: 2 givenname: Wenxi surname: Lu fullname: Lu, Wenxi email: luwx999@163.com organization: Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun, 130021, China – sequence: 3 givenname: Han surname: Wang fullname: Wang, Han organization: Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun, 130021, China – sequence: 4 givenname: Yukun surname: Bai fullname: Bai, Yukun organization: Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun, 130021, China |
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| Keywords | Differential evolution Auto xgboost Ensemble learning search Iterative ensemble smoother Particle filter Swarm evolution algorithm |
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| SubjectTerms | Auto xgboost computer software Differential evolution Ensemble learning search groundwater contamination Iterative ensemble smoother Particle filter simulation models Swarm evolution algorithm swarms trees |
| Title | Groundwater contaminant source identification based on an ensemble learning search framework associated with an auto xgboost surrogate |
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