基于自适应多保真度Co-Kriging代理模型的地下水污染源反演识别

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Abstract To identify groundwater pollution sources efficiently and accurately, the Co-Kriging method integrating Differential evolution was used to establish a multi-fidelity surrogate model based on comprehensive application of high fidelity and low fidelity numerical simulation models for solute transport. On this basis, the Markov chain Monte Carlo (MCMC)-DREAM_(D) algorithm and the adaptive updating multi fidelity surrogate model strategy were applied to identify groundwater pollution sources. To verify the effectiveness and feasibility of the above methods, this study conducted the numerical case study. The results showed that compared with the Kriging surrogate model based only on the input-output samples of the high fidelity model, the Co-Kriging surrogate model based on the joint use of input-output samples of the high fidelity and low fidelity model has higher approximation accuracy to the simulation model. The joint application of coupled multi fidelity Co-Kriging surrogate model and MCMC-DREAM_(D) algorithm can not only obtain accurate identification results, but also significantly reduce the calculation load. At the same time, the adaptive updating multi fidelity surrogate model strategy can further improve the identification accuracy for groundwater pollution sources.

Key words： groundwater pollution sources multi-fidelity proxy model Co-Kriging method DREAM_(D)algorithm self-adaption

Received: 21 July 2023

PACS:

X523

Corresponding Authors: 闫雪嫚,讲师,yanxm666@126.com E-mail: yanxm666@126.com

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	AN Yong-kai
	ZHANG Yan-xiang
	YAN Xue-man

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AN Yong-kai,ZHANG Yan-xiang,YAN Xue-man. Identification of groundwater pollution sources based on self-adaption Co-Kriging multi-fidelity surrogate model[J]. CHINA ENVIRONMENTAL SCIENCECE, 2024, 44(3): 1376-1385.

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http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2024/V44/I3/1376

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