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Type: Journal article
Title: Input determination for neural network models in water resources applications. Part 1 - background and methodology
Author: Bowden, G.
Dandy, G.
Maier, H.
Citation: Journal of Hydrology, 2005; 301(1-4):75-92
Publisher: Elsevier Science BV
Issue Date: 2005
ISSN: 0022-1694
Statement of
Gavin J. Bowden, Graeme C. Dandy and Holger R. Maier
Abstract: The use of artificial neural network (ANN) models in water resources applications has grown considerably over the last decade. However, an important step in the ANN modelling methodology that has received little attention is the selection of appropriate model inputs. This article is the first in a two-part series published in this issue and addresses the lack of a suitable input determination methodology for ANN models in water resources applications. The current state of input determination is reviewed and two input determination methodologies are presented. The first method is a model-free approach, which utilises a measure of the mutual information criterion to characterise the dependence between a potential model input and the output variable. To facilitate the calculation of dependence in the case of multiple inputs, a partial measure of the mutual information criterion is used. In the second method, a self-organizing map (SOM) is used to reduce the dimensionality of the input space and obtain independent inputs. To determine which inputs have a significant relationship with the output (dependent) variable, a hybrid genetic algorithm and general regression neural network (GAGRNN) is used. Both input determination techniques are tested on a number of synthetic data sets, where the dependence attributes were known a priori. In the second paper of the series, the input determination methodology is applied to a real-world case study in order to determine suitable model inputs for forecasting salinity in the River Murray, South Australia, 14 days in advance. © 2004 Elsevier B.V. All rights reserved.
Keywords: Artificial neural networks
Input determination
Self-organizing map
Genetic algorithm
Mutual information
General regression neural network
DOI: 10.1016/j.jhydrol.2004.06.021
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Appears in Collections:Aurora harvest 6
Civil and Environmental Engineering publications
Environment Institute publications

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