Simultaneous identification of groundwater contamination source and aquifer parameters with a new weighted–average wavelet variable–threshold denoising method
Simultaneous identification of groundwater contamination source and aquifer parameters with a new weighted–average wavelet variable–threshold denoising method
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DOI：
10.1007/s11356-021-12959-x
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发表时间：
2021-03
影响因子：
5.8
通讯作者：
Han Wang;Wenxi Lu;Zhenbo Chang
中科院分区：
文献类型：
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作者：
Han Wang;Wenxi Lu;Zhenbo Chang
首次提出了一种同时识别地下水污染源和含水层参数的并行启发式搜索策略。由于识别结果受噪声污染浓度数据等多种因素的影响，需要对数据进行去噪处理。现有的小波阈值去噪方法存在不可避免的缺陷，为此，本文首先提出了一种新的加权平均小波变阈值去噪方法(WWVD)，以改善对浓度数据的去噪效果，从而进一步提高后续的识别精度。然而，在似然计算过程中，频繁调用仿真模型可能会产生较高的计算成本。因此，为了降低成本，开发了仿真模型的单代理模型，但该模型存在局限性。为此，本文首先提出了一种差分进化-禁忌搜索(DE-TS)混合算法来构造最优集成代理模型，该算法将高斯过程、核极端学习机和支持向量回归结合在一起。首次提出的DE-TS算法也提高了代理模型对仿真模型的逼近精度。本文首次提出并实现了一种并行启发式搜索迭代过程用于同时辨识，并在迭代过程结束时得到辨识结果。通过一个假想案例验证了这些新方法的准确性和有效性。结果表明，WWVD方法不仅改善了浓度数据的去噪效果，而且提高了后续的识别精度。采用DE-TS混合算法的OES模型提高了代理模型对模拟模型的逼近精度，并行启发式搜索策略有助于同时识别地下水污染源和含水层参数。
This paper first proposed a parallel heuristic search strategy for simultaneous identification of groundwater contamination source and aquifer parameters. As identification results are influenced by many factors, such as noisy contamination concentration data, data denoising is necessary. The existing wavelet threshold denoising method has unavoidable shortcomings; therefore, this paper first proposed a new weighted–average wavelet variable–threshold denoising (WWVD) method to improve the denoising effect for concentration data, which further enhanced the subsequent identification accuracy. However, frequent calls to the simulation model could produce high computational cost during likelihood calculation. Hence, single surrogate model of the simulation model was developed to reduce cost; however, it presented limitation. Thus, this paper first developed a differential evolution–tabu search (DE-TS) hybrid algorithm to construct an optimal ensemble surrogate model, which assembled Gaussian process, kernel extreme learning machine, and support vector regression. The first proposed DE-TS algorithm also improved the approximation accuracy of surrogate model to simulation model. This paper first proposed and implemented a parallel heuristic search iterative process for simultaneous identification, and the identification results were obtained when the iteration process terminated. The accuracy and efficiency of these newly proposed approaches were tested through a hypothetical case. Results showed that the WWVD method not only improved the denoising effect for concentration data but also enhanced the subsequent identification accuracy. The OES model using DE-TS hybrid algorithm improved the approximation accuracy of surrogate model to simulation model, and the parallel heuristic search strategy is helpful for simultaneous identification of groundwater contamination source and aquifer parameters.