Application of artificial neural networks to groundwater dynamics in coastal aquifers

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Title Application of artificial neural networks to groundwater dynamics in coastal aquifers
Author Joorabchi, Amirhassan; Zhang, Hong; Blumenstein, Michael Myer
Journal Name Journal of Coastal Research
Year Published 2009
Place of publication United States
Publisher Coastal Education & Research Foundation
Abstract In the present study, Artificial Neural Networks (ANNs) are adopted to simulate groundwater table fluctuations. A multilayer feed-forward neural network model has been developed and trained using a back-propagation algorithm. The training data was based on field measurements (KANG et al., 1994) from five different locations down the east coast of Australia. The data included information on watertable, tide elevation, beach slopes and hydraulic conductivity at each beach. The results from the developed model show that the artificial neural network model is very successful in terms of the prediction of a target that is dependent on a number of variables. Sensitivity analysis was undertaken which confirmed that a variation in tide elevation is the most important parameter to use for simulating groundwater levels in coastal aquifers.
Peer Reviewed Yes
Published Yes
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Copyright Statement Copyright 2009 CERF. The attached file is reproduced here in accordance with the copyright policy of the publisher. Please refer to the journal's website for access to the definitive, published version.
Volume SI 56
Issue Number 2
Page from 966
Page to 970
ISSN 0749-0258
Date Accessioned 2009-10-19
Language en_AU
Research Centre Institute for Integrated and Intelligent Systems
Faculty Faculty of Science, Environment, Engineering and Technology
Subject Simulation and Modelling; Water Resources Engineering
Publication Type Journal Articles (Refereed Article)
Publication Type Code c1

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