Groundwater pollution sources inversion based on local-global hybrid adaptive surrogate model
LUO Jian-nan1, LI Xue-li1, WANG He2, MA Xi1, SONG Zhuo1
1. College of New Energy and Environment, Jilin University, Changchun 130021, China; 2. Jilin Yunhe Environmental Protection Technology Co., Ltd, Changchun 130000, China
Abstract：A local-global hybrid adaptive surrogate model was proposed based on optimal solution criterion and cross validation- Voronoi (CV-Voronoi) criterion to improve the inversion accuracy and computational efficiency of groundwater pollution source. The local-global hybrid adaptive surrogate model combined with genetic algorithm was applied to groundwater pollution sources inversion case. The inversion results were compared with those of the local and the global adaptive surrogate model. The comparison results reveal that the local-global hybrid adaptive surrogate model combined genetic algorithm had the highest inversion accuracy and the lowest computational cost. The pollution sources inversion results can identify the actual pollution source characteristics, and the maximum relative error was only 3.51%. The results of the paper prove the robustness of the proposed local-global hybrid adaptive surrogate model in improving the accuracy and computational efficiency of groundwater pollution sources inversion.
罗建男, 李雪利, 王鹤, 马溪, 宋卓. 基于局部-全局混合自适应替代模型的地下水污染源反演识别[J]. 中国环境科学, 2023, 43(7): 3664-3671.
LUO Jian-nan, LI Xue-li, WANG He, MA Xi, SONG Zhuo. Groundwater pollution sources inversion based on local-global hybrid adaptive surrogate model. CHINA ENVIRONMENTAL SCIENCECE, 2023, 43(7): 3664-3671.
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