Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/1067
Citations
Scopus Web of ScienceĀ® Altmetric
?
?
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
Responsibility: 
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; forecasting; salinity model
Rights: Copyright 2002 by the American Geophysical Union
RMID: 0020020674
DOI: 10.1029/2001WR000266
Published version: http://www.agu.org/pubs/crossref/2002/2001WR000266.shtml
Appears in Collections:Civil and Environmental Engineering publications
Environment Institute publications

Files in This Item:
File Description SizeFormat 
hdl_1067.pdfPublished version175.12 kBAdobe PDFView/Open
vers_hdl_1067.pdfVersion information12.24 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.