Identification of unknown groundwater pollution sources using artificial neural networks
Singh, Raj Mohan, Datta, Bithin, and Jain, Ashu (2004) Identification of unknown groundwater pollution sources using artificial neural networks. Journal of Water Resources Planning and Management, 130 (6). pp. 506-514.
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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.
|Item Type:||Article (Refereed Research - C1)|
|Keywords:||ground-water pollution; pollution sources; neural networks; data analysis|
|Date Deposited:||10 Apr 2010 00:50|
|FoR Codes:||09 ENGINEERING > 0905 Civil Engineering > 090509 Water Resources Engineering @ 50%
09 ENGINEERING > 0907 Environmental Engineering > 090702 Environmental Engineering Modelling @ 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%