The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
Optimal division of data for neural network models in water resources applications
Open accessAbstract
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.
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Cites
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Cited by
Maier, H. R., Jain, A., Dandy, G. C., & Sudheer, K. P. (2010) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2010.02.003 · Cited by 871
Self-citationA survey on river water quality modelling using artificial intelligence models: 2000–2020
Tiyasha, Tung, T. M., & Yaseen, Z. M. (2020) · Journal of Hydrology · doi:10.1016/j.jhydrol.2020.124670 · Cited by 650
Kalteh, A. M., Hjorth, P., & Berndtsson, R. (2008) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2007.10.001 · Cited by 522
A review of artificial neural network models for ambient air pollution prediction
Cabaneros, S. M., Calautit, J. K., & Hughes, B. R. (2019) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2019.06.014 · Cited by 459
Abrahart, R. J., Anctil, F., Coulibaly, P., Dawson, C. W., Mount, N. J., See, L. M., Shamseldin, A. Y., Solomatine, D. P., Toth, E., & Wilby, R. L. (2012) · Progress in Physical Geography: Earth and Environment · doi:10.1177/0309133312444943 · Cited by 296
Metadata
- Authors
- Bowden, G. J., Maier, H. R., & Dandy, G. C.
- Venue
- Water Resources Research
- Publisher
- American Geophysical Union (AGU)
- Published
- 2002-02
- Volume
- 38
- Issue
- 2
- ISSN
- 0043-1397 (ISSN-L)
- 1944-7973
- OpenAlex
- W1912463333
- Cited by
- 322
- References
- 25
- FWCI
- 18.14
- Type
- journal article
- Language
- en
- Open access
- bronze
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
