The DOI matches its Crossref record; 113 of the 113 references it cites were found in a registry or an index.
Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions
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Citations
Cites
Neural networks for pattern recognition
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Maier, H. R., & Dandy, G. C. (2000) · Environmental Modelling & Software · doi:10.1016/s1364-8152(99)00007-9 · Cited by 2,377
Self-citationNeural Networks: A Systematic Introduction
Rojas, R. (1996) · Medical Entomology and Zoology · Cited by 2,138
Fundamentals of Artificial Neural Networks
Hassoun, M. H. (1996) · Proceedings of the IEEE · doi:10.1109/jproc.1996.503146 · Cited by 1,887
Practical neural network recipes in C
Masters, T. D. (1993) · Cited by 1,636
Artificial Neural Networks in Hydrology. I: Preliminary Concepts
ASCE Task Committee on Application of Artificial Neural Networks in Hydrology (2000) · Journal of Hydrologic Engineering · doi:10.1061/(asce)1084-0699(2000)5:2(115 · Cited by 1,476
Artificial Neural Networks in Hydrology. II: Hydrologic Applications
ASCE Task Committee on Application of Artificial Neural Networks in Hydrology (2000) · Journal of Hydrologic Engineering · doi:10.1061/(asce)1084-0699(2000)5:2(124 · Cited by 1,339
Ten iterative steps in development and evaluation of environmental models
Jakeman, A. J., Letcher, R. A., & Norton, J. P. (2006) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2006.01.004 · Cited by 1,111
Hydrological modelling using artificial neural networks
Dawson, C. W., & Wilby, R. L. (2001) · Progress in Physical Geography: Earth and Environment · doi:10.1177/030913330102500104 · Cited by 903
Handbook of Neural Computing Applications
(1990) · Elsevier eBooks · doi:10.1016/c2013-0-11292-5 · Cited by 706
Cited by
Tyralis, H., Papacharalampous, G., & Langousis, A. (2019) · Water · doi:10.3390/w11050910 · Cited by 771
Bayesian networks in environmental modelling
Aguilera, P. A., Fernández, A., Fernández, R., Rumí, R., & Salmerón, A. (2011) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2011.06.004 · Cited by 666
A 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
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
Taormina, R., Chau, K.-W., & Sethi, R. (2012) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2012.02.009 · Cited by 396
Metadata
- Authors
- Maier, H. R., Jain, A., Dandy, G. C., & Sudheer, K. P.
- Venue
- Environmental Modelling & Software
- Publisher
- Elsevier BV
- Published
- 2010-08
- Volume
- 25
- Issue
- 8
- Pages
- 891-909
- ISSN
- 1364-8152 (ISSN-L)
- OpenAlex
- W2095239580
- Cited by
- 871
- References
- 113
- FWCI
- 28.92
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions. https://app.cochat.ai/papers/10.1016/j.envsoft.2010.02.003
- The paper
- Maier, H. R., Jain, A., Dandy, G. C., & Sudheer, K. P. (2010). Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions. Environmental Modelling & Software. https://doi.org/10.1016/j.envsoft.2010.02.003
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
