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Identification of Groundwater Pollution Sources by a SCE-UA Algorithm-Based Simulation/Optimization Model

文献类型：期刊论文

作者Huang, Linxian2,3; Wang, Lichun4; Zhang, Yongyong5; Xing, Liting2,3; Hao, Qichen6; Xiao, Yong7; Yang, Lizhi1; Zhu, Henghua1
刊名WATER
出版日期2018-02-01
卷号10期号:2页码:19
关键词groundwater pollution inverse problem SCE-UA S/O model Grids Traversal algorithm
ISSN号2073-4441
DOI10.3390/w10020193
通讯作者Huang, Linxian(stu_huanglx@ujn.edu.cn) ; Zhang, Yongyong(zhangyy003@igsnrr.ac.cn)
英文摘要Prevention and remediation strategies for groundwater pollution can be successfully carried out if the location, concentration, and release history of contaminants can be accurately identified. This, however, presents a challenge due to complex groundwater systems. To address this issue, a simulation-optimization (S/O) model by integrating MODFLOW and MT3DMS into a shuffled complex evolution (SCE-UA) optimization algorithm was proposed; this coupled model can identify the unknown groundwater pollution source characteristics. Moreover, the Grids Traversal algorithm was used for automatically searching all possible combinations of pollution source location. The performance of the proposed S/O model was tested by three hypothetical scenarios with various combinations of mixed situations (i.e., single and multiple pollution source locations, known and unknown pollution source locations, steady-state flow and transient flow). The field measurement errors was additionally considered and analyzed. Our results showed that this proposed S/O model performed reasonably well. The identified locations and concentrations of contaminants fairly matched with the imposed inputs with average normalized deviations less than 1% after sufficient generations. We further assessed the impact of generation number on the performance of the S/O model. The performance could be significantly improved by increasing generation number, which yet resulted in a heavy computational burden. Furthermore, the proposed S/O model performed more efficiently and robustly than the traditionally used artificial neural network (ANN)-based model. This is due to the internal linkage of numerical simulation in the S/O model that promotes the data exchange from external files to programming variables. This new model allows for solving the source-identification problems considering complex conditions, and thus for providing a platform for groundwater pollution prevention and management.
WOS关键词RAINFALL-RUNOFF MODELS ; GLOBAL OPTIMIZATION METHOD ; NORTH CHINA PLAIN ; PARAMETER OPTIMIZATION ; HYDROLOGICAL MODEL ; TANK MODEL ; CALIBRATION ; BASIN ; SIMULATION ; MANAGEMENT
WOS研究方向Water Resources
语种英语
WOS记录号WOS:000426775500103
出版者MDPI
源URL[http://ir.igsnrr.ac.cn/handle/311030/57174]  
专题中国科学院地理科学与资源研究所
通讯作者Huang, Linxian; Zhang, Yongyong
作者单位1.Shandong Inst Geol Survey, Jinan 250000, Shandong, Peoples R China
2.Univ Jinan, Sch Resources & Environm, Jinan 250022, Shandong, Peoples R China
3.Engn Technol Inst Groundwater Numer Simulat & Con, Jinan 250022, Shandong, Peoples R China
4.Univ Texas, Dept Geol Sci, Austin, TX 78705 USA
5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
6.CAGS, Inst Hydrogeol & Environm Geol, Shijiazhuang 050000, Hebei, Peoples R China
7.Southwest Jiaotong Univ, Fac Geosci & Environm Engn, Chengdu 610031, Sichuan, Peoples R China
推荐引用方式
GB/T 7714		 Huang, Linxian,Wang, Lichun,Zhang, Yongyong,et al. Identification of Groundwater Pollution Sources by a SCE-UA Algorithm-Based Simulation/Optimization Model[J]. WATER,2018,10(2):19.
APA Huang, Linxian.,Wang, Lichun.,Zhang, Yongyong.,Xing, Liting.,Hao, Qichen.,...&Zhu, Henghua.(2018).Identification of Groundwater Pollution Sources by a SCE-UA Algorithm-Based Simulation/Optimization Model.WATER,10(2),19.
MLA Huang, Linxian,et al."Identification of Groundwater Pollution Sources by a SCE-UA Algorithm-Based Simulation/Optimization Model".WATER 10.2(2018):19.

入库方式： OAI收割

来源：地理科学与资源研究所

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