A Hybrid Artificial Neural Network-Numerical Model for Ground Water Problems

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dc.contributor.author Szidarovszky, Ferenc en_US
dc.contributor.author Coppola, E en_US
dc.contributor.author Long, J en_US
dc.contributor.author Poulton, M en_US
dc.contributor.author Hall, Tony en_US
dc.date.accessioned 2009-06-26T04:11:10Z
dc.date.available 2009-06-26T04:11:10Z
dc.date.issued 2007 en_US
dc.identifier 2006013565 en_US
dc.identifier.citation Szidarovszky Ferenc et al. 2007, 'A Hybrid Artificial Neural Network-Numerical Model for Ground Water Problems', Wiley-Blackwell Publishing, vol. 45, no. 5, pp. 590-600. en_US
dc.identifier.issn 0017-467X en_US
dc.identifier.other C1 en_US
dc.identifier.uri http://hdl.handle.net/10453/672
dc.description.abstract Numerical models constitute the most advanced physical-based methods for modeling complex ground water systems. Spatial and/or temporal variability of aquifer parameters, boundary conditions, and initial conditions (for transient simulations) can be assigned across the numerical model domain. While this constitutes a powerful modeling advantage, it also presents the formidable challenge of overcoming parameter uncertainty, which, to date, has not been satisfactorily resolved, inevitably producing model prediction errors. In previous research, artificial neural networks (ANNs), developed with more accessible field data, have achieved excellent predictive accuracy over discrete stress periods at site-specific field locations in complex ground water systems. In an effort to combine the relative advantages of numerical models and ANNs, a new modeling paradigm is presented. The ANN models generate accurate predictions for a limited number of field locations. Appending them to a numerical model produces an overdetermined system of equations, which can be solved using a variety of mathematical techniques, potentially yielding more accurate numerical predictions. Mathematical theory and a simple two-dimensional example are presented to overview relevant mathematical and modeling issues. Two of the three methods for solving the overdetermined system achieved an overall improvement in numerical model accuracy for various levels of synthetic ANN errors using relatively few constrained head values (i.e., cells), which, while demonstrating promise, requires further research. This hybrid approach is not limited to ANN technology; it can be used with other approaches for improving numerical model predictions, such as regression or support vector machines (SVMs). en_US
dc.publisher Wiley-Blackwell Publishing en_US
dc.relation.isbasedon http://dx.doi.org/10.1111/j.1745-6584.2007.00330.x en_US
dc.title A Hybrid Artificial Neural Network-Numerical Model for Ground Water Problems en_US
dc.parent Journal of Ground Water en_US
dc.journal.volume 45 en_US
dc.journal.number 5 en_US
dc.publocation Oxford en_US
dc.identifier.startpage 590 en_US
dc.identifier.endpage 600 en_US
dc.cauo.name BUS.KURC: Quantitative Finance Research Centre en_US
dc.conference Verified OK en_US
dc.for 040600 en_US
dc.personcode 100815 en_US
dc.personcode 0000034388 en_US
dc.personcode 0000034389 en_US
dc.personcode 970384 en_US
dc.personcode 0000034390 en_US
dc.percentage 100 en_US
dc.classification.name Physical Geography and Environmental Geoscience en_US
dc.classification.type FOR-08 en_US
dc.location.activity ISI:000248956600010 en_US


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