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Type: Journal article
Title: Optimal division of data for neural network models in water resources applications
Author: Bowden, G.
Maier, H.
Dandy, G.
Citation: Water Resources Research, 2002; 38(2):2.1-2.11
Publisher: Amer Geophysical Union
Issue Date: 2002
ISSN: 0043-1397
Statement of
Gavin J. Bowden, Holger R. Maier and Graeme C. Dandy
Abstract: The way that available data are divided into training, testing, and validation subsets can have a significant influence on the performance of an artificial neural network (ANN). Despite numerous studies, no systematic approach has been developed for the optimal division of data for ANN models. This paper presents two methodologies for dividing data into representative subsets, namely, a genetic algorithm (GA) and a self-organizing map (SOM). These two methods are compared with the conventional approach commonly used in the literature, which involves an arbitrary division of the data. A case study is presented in which ANN models developed using each data division technique are used to forecast salinity in the River Murray at Murray Bridge (South Australia) 14 days in advance. When tested on a validation data set from July 1992 to March 1998, the models developed using the GA and SOM data division techniques resulted in a reduction in RMS error of 24.2% and 9.9%, respectively, over the conventional data division method. It was found that a SOM could be used to diagnose why an ANN model has performed poorly, given that the poor performance is primarily related to the data themselves and not the choice of the ANN's parameters or architecture.
Keywords: artificial neural network
data division
self-organizing map
genetic algorithm
salinity model
Rights: Copyright 2002 by the American Geophysical Union
DOI: 10.1029/2001WR000266
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