Groundwater pollution source identification and simultaneous parameter estimation using pattern matching by artificial neural network
Singj, Raj Mohan, and Datta, Bithin (2004) Groundwater pollution source identification and simultaneous parameter estimation using pattern matching by artificial neural network. Enviromental Forensics, 5. pp. 143-153.
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Pollution of groundwater often occurs because of unknown disposal of toxicwastes, 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.Atrained 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.
|Item Type:||Article (Refereed Research - C1)|
|Keywords:||groundwater pollution, pollution source identification, artificial neural networks, concentration patterns, inverse problem|
|Date Deposited:||14 Apr 2010 01:27|
|FoR Codes:||09 ENGINEERING > 0907 Environmental Engineering > 090702 Environmental Engineering Modelling @ 50%
09 ENGINEERING > 0905 Civil Engineering > 090509 Water Resources Engineering @ 50%
|SEO Codes:||96 ENVIRONMENT > 9606 Environmental and Natural Resource Evaluation > 960604 Environmental Management Systems @ 50%
96 ENVIRONMENT > 9609 Land and Water Management > 960999 Land and Water Management of Environments not elsewhere classified @ 25%
96 ENVIRONMENT > 9605 Ecosystem Assessment and Management > 960506 Ecosystem Assessment and Management of Fresh, Ground and Surface Water Environments @ 25%