Groundwater Pollution Source Identification and Simultaneous Parameter Estimation Using Pattern Matching by Artificial Neural Network
Groundwater Pollution Source Identification and Simultaneous Parameter Estimation Using Pattern Matching by Artificial Neural Network
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DOI：
10.1080/15275920490495873
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发表时间：
2004-09
影响因子：
1.8
通讯作者：
R. Singh;B. Datta
中科院分区：
文献类型：
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作者：
R. Singh;B. Datta
地下水的污染往往是由于有毒废物的不明处置，特别是来自工业场地的有毒废物，或由于管道、废物储存容器或地下罐的未被发现的泄漏。仅使用含水层的浓度测量数据来确定污染源类似于重建含水层在一段时间内发生的事件的历史。当水文地质水流和运移参数也未知时，识别未知地下水污染源变得更加困难。训练好的人工神经网络（ANN）可以同时解决未知地下水污染源的估计和未知水文地质参数（导水率、孔隙度和弥散度）的估计问题。在这篇文章中，通用函数逼近性能的多层，前馈神经网络被用来估计时间和空间变化的未知污染源，以及提供一个可靠的估计未知的流动和传输参数。人工神经网络使用反向传播算法的模拟数据的模式进行训练。一组源通量和随时间变化的模拟浓度测量构成的模式进行训练。本文还描述了一个潜在的适用性，这种方法通过使用一个说明性的例子。此外，在不同的浓度测量误差下评价了方法学性能。有限的性能评估表明，所提出的方法执行合理，即使有很大的测量误差。
Pollution of groundwater often occurs because of unknown disposal of toxic wastes, especially from industrial sites, or due to undetected leakage from pipes, waste storage containers, or underground tanks. The determination of pollution sources by using only concentration measurement data in the aquifer is analogous to reconstructing the history of events that has occurred in the aquifer over a time horizon. Identification of unknown groundwater pollution sources becomes more difficult when the hydrogeologic flow and transport parameters are also unknown. A trained artificial neural network (ANN) can be utilized to simultaneously solve the problems of estimating unknown groundwater pollution sources and estimating unknown hydrogeologic parameters (hydraulic conductivity, porosity, and dispersivities). In this article, the universal function approximation property of a multilayer, feed-forward ANN was utilized to estimate temporally and spatially varying unknown pollution sources, as well as to provide a reliable estimation of unknown flow and transport parameters. ANN was trained on patterns of simulated data using a back-propagation algorithm. A set of source fluxes and temporally varying simulated concentration measurements constituted the pattern for training. This article also describes a potential applicability of this methodology by using an illustrative example. Additionally, the methodology performance is evaluated under varying concentration measurement errors. The limited performance evaluations show that the proposed methodology performs reasonably well even with large measurement errors.