摘要
为高效率高精度地进行地下水污染源反演识别,综合运用高保真度和低保真度地下水溶质运移数值模拟模型,研究应用集成差分进化算法的Co-Kriging方法建立模拟模型的多保真度代理模型;在此基础上,探索应用马尔科夫链蒙特卡洛(MCMC)-DREAM_((D))算法,并采用自适应更新多保真度代理模型策略进行地下水污染源反演识别.为验证上述方法的有效性和可行性,开展了数值算例研究.结果表明:相比仅基于高保真度模型输入-输出样本构建的Kriging代理模型,联合运用高保真度和低保真度模型输入-输出样本构建的Co-Kriging代理模型对模拟模型的逼近精度更高;耦合多保真度Co-Kriging代理模型和MCMC-DREAM_((D))算法能够得到较高精度的污染源反演结果,且能够大幅度减小计算负荷;同时,采用自适应更新多保真度代理模型策略能够进一步提高污染源反演识别精度.
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.
作者
安永凯
张岩祥
闫雪嫚
AN Yong-kai;ZHANG Yan-xiang;YAN Xue-man(School of Water and Environment,Chang’an University,Xi’an 710054,China;Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid Region of the Ministry of Education,Chang’an University,Xi’an 710054,China;Power China Northwest Engineering Corporation Limited,Xi’an 710065,China;College of Urban and Environmental Sciences,Northwest University,Xi’an 710127,China)
出处
《中国环境科学》
EI
CAS
CSCD
北大核心
2024年第3期1376-1385,共10页
China Environmental Science
基金
国家自然科学基金资助项目(42102287)
中国博士后基金项目(2020M683399)
陕西省自然科学基础研究计划(2023-JC-QN-0290)。
