Identifying the release history of a groundwater contaminant source based on an ensemble surrogate model
Identifying the release history of a groundwater contaminant source based on an ensemble surrogate model
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基于集合替代模型识别地下水污染物源的释放历史
DOI：
10.1016/j.jhydrol.2019.03.020
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发表时间：
2019
影响因子：
6.4
通讯作者：
Lu Wenxi
中科院分区：
文献类型：
--
作者：
Xing Zhenxiang;Qu Ruizhuo;Zhao Ying;Fu Qiang;Ji Yi;Lu Wenxi
在地下水污染源识别中，针对模拟模型计算效率低的问题，提出了一种集合代理模型，以提高结果的准确性和稳健性。本文提出的集成代理模型由以下三个个体代理模型组成：克里金法、径向基函数和最小二乘支持向量机。采用自适应Metropolis-马尔可夫链蒙特卡罗方法对三种模型进行权重分配。不仅对保守污染物，而且对含有化学反应的污染物，对集合代理模型的准确性和稳健性进行了检验。结果表明，该集成替代模型是解决逆污染源识别问题的一种有效方法，具有较高的精度和较短的计算时间。
In identifying groundwater contaminant sources, given that the simulation model is computationally inefficient, an ensemble surrogate model is proposed to improve the accuracy and robustness of results. The proposed ensemble surrogate model in this paper consists of the following three individual surrogate models: Kriging, radial basis functions and least squares support vector machines. The Adaptive Metropolis-Markov Chain Monte Carlo method is used to assign weights to the three models. Accuracy and robustness of the ensemble surrogate model were tested on not only conservative contaminants but also contaminants containing chemical reaction. The results indicated that the proposed ensemble surrogate model is an effective method to solve the inverse contaminant source identification problems with a high degree of accuracy and short computation time.