Identification of Unknown Groundwater Pollution Sources Using Artificial Neural Networks
Identification of Unknown Groundwater Pollution Sources Using Artificial Neural Networks
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DOI：
10.1061/(asce)0733-9496(2004)130:6(506
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发表时间：
2004-10
影响因子：
3.1
通讯作者：
R. Singh;B. Datta;Ashu Jain
中科院分区：
文献类型：
--
作者：
R. Singh;B. Datta;Ashu Jain
未知地下水污染源的时间和空间特征仍然是有效含水层修复和相关健康风险评估的一个重要问题。污染源的表征涉及识别空间和时间上变化的源位置、注入速率和释放周期。所提出的方法利用前馈多层人工神经网络（ANN）的通用函数逼近能力来识别未知污染源。人工神经网络经过训练，可以根据含水层中指定观测位置的模拟污染物浓度测量数据来识别源特征。这些浓度是针对大量随机生成的污染源通量进行模拟的。反向传播算法用于训练 ANN，每组相应的源通量和产生的浓度测量构成训练 ANN 的模式。该方法的性能针对各种数据可用性、测量误差和源位置场景进行评估。所开发的人工神经网络能够使用错误的测量数据识别多个地点的未知地下水污染源。
The temporal and spatial characterization of unknown groundwater pollution sources remains an important problem in effective aquifer remediation and assessment of associated health risks. The characterization of contaminated source involves identifying spatially and temporally varying source locations, injection rates, and release periods. The proposed methodology exploits the universal function approximation capability of a feed forward multilayer artificial neural network (ANN) to identify the unknown pollution sources. The ANN is trained to identify source characteristics based on simulated contaminant concentration measurement data at specified observation locations in the aquifer. These concentrations are simulated for a large set of randomly generated pollution source fluxes. The back-propagation algorithm is used for training the ANN, with each corresponding set of source fluxes and resulting concentration measurement constituting a pattern for training the ANN. Performance of this methodology is evaluated for various data availability, measurement error, and source location scenarios. The developed ANNs are capable of identifying unknown groundwater pollution sources at multiple locations using erroneous measurement data.