The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Artificial Neural Networks in Hydrology. I: Preliminary Concepts
Abstract
In this two-part series, the writers investigate the role of artificial neural networks (ANNs) in hydrology. ANNs are gaining popularity, as is evidenced by the increasing number of papers on this topic appearing in hydrology journals, especially over the last decade. In terms of hydrologic applications, this modeling tool is still in its nascent stages. The practicing hydrologic community is just becoming aware of the potential of ANNs as an alternative modeling tool. This paper is intended to serve as an introduction to ANNs for hydrologists. Apart from descriptions of various aspects of ANNs and some guidelines on their usage, this paper offers a brief comparison of the nature of ANNs and other modeling philosophies in hydrology. A discussion on the strengths and limitations of ANNs brings out the similarities they have with other modeling approaches, such as the physical model.
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Cites
Neural networks and physical systems with emergent collective computational abilities.
Hopfield, J. J. (1982) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.79.8.2554 · Cited by 19,270
A logical calculus of the ideas immanent in nervous activity
McCulloch, W. S., & Pitts, W. (1943) · The Bulletin of Mathematical Biophysics · doi:10.1007/bf02478259 · Cited by 18,566
Neural Networks for Pattern Recognition
Bishop, C. M. (1995) · doi:10.1093/oso/9780198538493.001.0001 · Cited by 12,287
Generalization of back-propagation to recurrent neural networks
Pineda, F. J. (1987) · Physical Review Letters · doi:10.1103/physrevlett.59.2229 · Cited by 931
Cited by
Kalteh, A. M., Hjorth, P., & Berndtsson, R. (2008) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2007.10.001 · Cited by 522
Taormina, R., Chau, K.-W., & Sethi, R. (2012) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2012.02.009 · Cited by 396
Drought forecasting using artificial neural networks and time series of drought indices
Morid, S., Smakhtin, V., & Bagherzadeh, K. (2007) · International Journal of Climatology · doi:10.1002/joc.1498 · Cited by 337
Neural network modeling of hydrological systems: A review of implementation techniques
Oyebode, O., & Stretch, D. (2019) · Natural Resource Modeling · doi:10.1111/nrm.12189 · Cited by 111
Basak, J. K., Okyere, F. G., Arulmozhi, E., Park, J., Khan, F., & Kim, H. T. (2020) · Journal of Applied Animal Research · doi:10.1080/09712119.2020.1761818 · Cited by 39
Metadata
- Authors
- ASCE Task Committee on Application of Artificial Neural Networks in Hydrology
- Venue
- Journal of Hydrologic Engineering
- Published
- 2000-04
- Volume
- 5
- Issue
- 2
- Pages
- 115-123
- ISSN
- 1084-0699 (ISSN-L)
- 1943-5584
- DOI
- 10.1061/(asce)1084-0699(2000)5:2(115
- OpenAlex
- W3017323153
- Cited by
- 1,476
- References
- 4
- FWCI
- 24.40
- Type
- journal article
- Language
- en
- Open access
- closed
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- ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. (2000). Artificial Neural Networks in Hydrology. I: Preliminary Concepts. Journal of Hydrologic Engineering. https://doi.org/10.1061/(asce)1084-0699(2000)5%3A2(115
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