中国农村水利水电Issue(5)：60-67,8.DOI:10.12396/znsd.2500976
基于自适应LSTM代理模型和MCMC方法的地下水污染源反演识别研究
Groundwater Pollution Source Inversion Identification Based on Adaptive LSTM Proxy Model and MCMC Method
摘要
Abstract
To efficiently and accurately conduct groundwater pollution source inversion,this paper employs deep learning methods,specifically the Long Short-Term Memory(LSTM)model and Multilayer Perceptron(MLP),to establish proxy models for pollution transport simulation.The DREAM-MCMC algorithm is then applied,using an adaptive update strategy to identify the groundwater pollution source inversion results.Finally,sensitivity analysis is used to discuss the inversion outcomes,thereby constructing a comprehensive groundwater pollution source inversion system.Two numerical examples are used to validate the proposed system.The results show that the LSTM-based proxy model achieves higher accuracy in simulating the model.Specifically,in Example 1,the three evaluation metrics—coefficient of determination(R²),Mean Squared Error(MSE),and Mean Relative Error(MRE)—reach 0.999 9,0.03 and 0.001,respectively,while in Example 2,the values are 0.883 4,333.65 and 0.362.In comparison,the proxy model built using the MLP method in Example 1 has values of 0.999 1,0.76 and 0.005,and in Example 2,the values are 0.810 3,665.42 and 0.262.These results demonstrate that the proxy model built using LSTM achieves higher approximation accuracy for the simulation model.By combining the DREAM-MCMC algorithm with the adaptive update strategy,and comparing it to the inversion method without the adaptive update strategy,the results indicate that the method with the adaptive update strategy generally exhibits lower relative errors in the inversion outcomes,confirming that this strategy significantly improves the inversion accuracy.Finally,the sensitivity analysis of the inversion results further elucidated the relationship between them.The results from the two numerical examples prove that this system can efficiently and accurately solve groundwater pollution source inversion problems,providing new insights into addressing groundwater pollution.关键词
地下水污染源反演/长短期记忆网络/代理模型/DREAM-MCMC算法/自适应更新策略Key words
inversion of groundwater pollution sources/Long Short-Term Memory(LSTM)network/proxy model/DREAM-MCMC algorithm/adaptive update algorithm分类
资源环境引用本文复制引用
鄢宇鑫,安永凯,闫雪嫚..基于自适应LSTM代理模型和MCMC方法的地下水污染源反演识别研究[J].中国农村水利水电,2026,(5):60-67,8.基金项目
国家自然科学基金资助项目(42302275,42102287) （42302275,42102287）
中国博士后基金项目(2020M683399) （2020M683399）
陕西省自然科学基础研究计划项目(2023-JC-QN-0290). （2023-JC-QN-0290）
