Application of artificial neural networks to groundwater dynamics in coastal aquifers

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Joorabchi, A
Zhang, H
Blumenstein, M
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2009
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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.

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Journal of Coastal Research
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© 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.
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Subject
Earth sciences
Modelling and simulation
Engineering
Water resources engineering
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