Three-Dimensional Groundwater Contamination Source Identification Using Adaptive Simulated Annealing
Three-Dimensional Groundwater Contamination Source Identification Using Adaptive Simulated Annealing
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DOI：
10.1061/(asce)he.1943-5584.0000624
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发表时间：
2013-03
影响因子：
2.4
通讯作者：
M. Jha;B. Datta
中科院分区：
文献类型：
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作者：
M. Jha;B. Datta
从地下水污染物浓度观测资料中确定未知地下水污染源的释放历史等地下水污染源特性是一个反问题。这类反问题的解往往是不唯一的，因而是一个不适定问题。一个链接的模拟优化方法可以用来有效地解决这个问题。然而，这种方法是计算密集型的，所获得的结果往往是非常容易受到误差的测量数据和估计的水文地质参数。除此之外，解决方案的准确性在很大程度上取决于监测位置的选择。一个自适应模拟退火（阿萨）为基础的解决方案的算法被证明是计算效率最佳的源特性的识别在执行时间和精度方面。这种计算效率似乎占上风，即使在估计的参数和浓度测量误差的误差水平适中。此外，污染物浓度监测位置被证明是关键的未知污染源的有效表征。不同的监测网络的最佳识别结果，以证明一个网络适合有效的源识别的相关性。
Determination of groundwater contaminant source characteristics such as release histories of unknown groundwater pollutant sources from concentration observation data is an inverse problem. Often solution to this inverse problem is nonunique, and it is an ill-posed problem. A linked simulation-optimization approach can be used to solve this problem efficiently. However, this approach is computationally intensive, and the results obtained tend to be highly susceptible to errors in the measured data and estimated hydrogeological parameters. Apart from this, accuracy of the solutions is highly dependent on the choice of monitoring locations. An adaptive simulated annealing (ASA)-based solution algorithm is shown to be computationally efficient for optimal identification of the source characteristics in terms of execution time and accuracy. This computational efficiency appears to prevail even with moderate levels of errors in estimated parameters and concentration measurement errors. Also, the contaminant concentration monitoring locations are shown to be critical in the efficient characterization of the unknown contaminant sources. Optimal identification results for different monitoring networks are presented to demonstrate the relevance of a network suitable for efficient source identification.