The DOI matches its Crossref record; 131 of the 131 references it cites were found in a registry or an index.
Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications
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Citations
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Estimating the Dimension of a Model
Schwarz, G. (1978) · The Annals of Statistics · doi:10.1214/aos/1176344136 · Cited by 47,033
Optimization by Simulated Annealing
Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983) · Science · doi:10.1126/science.220.4598.671 · Cited by 45,134
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
Multilayer feedforward networks are universal approximators
Hornik, K., Stinchcombe, M., & White, H. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90020-8 · Cited by 21,728
Time Series Analysis: Forecasting and Control
Geurts, M., Box, G. E. P., & Jenkins, G. M. (1977) · Journal of Marketing Research · doi:10.2307/3150485 · Cited by 19,186
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
Learning Internal Representations by Error Propagation
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1985) · doi:10.21236/ada164453 · Cited by 16,172
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Cross validatory choice and assessment of statistical predictions
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Cited by
Machine Learning Methods Without Tears: A Primer for Ecologists
Olden, J. D., Lawler, J. J., & Poff, N. L. (2008) · The Quarterly Review of Biology · doi:10.1086/587826 · Cited by 823
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
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
Deo, R. C., & Şahin, M. (2015) · Atmospheric Research · doi:10.1016/j.atmosres.2014.10.016 · Cited by 317
Novel approach for streamflow forecasting using a hybrid ANFIS-FFA model
Yaseen, Z. M., Ebtehaj, I., Bonakdari, H., Deo, R. C., Danandeh Mehr, A., Mohtar, W. H. M. W., Diop, L., El-shafie, A., & Singh, V. P. (2017) · Journal of Hydrology · doi:10.1016/j.jhydrol.2017.09.007 · Cited by 267
Metadata
- Authors
- Maier, H. R., & Dandy, G. C.
- Venue
- Environmental Modelling & Software
- Publisher
- Elsevier BV
- Published
- 2000-01
- Volume
- 15
- Issue
- 1
- Pages
- 101-124
- ISSN
- 1364-8152 (ISSN-L)
- OpenAlex
- W1998442441
- Cited by
- 2,377
- References
- 131
- FWCI
- 45.32
- Type
- journal article
- Language
- en
- Open access
- closed
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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- CoChat. (2026). Citation check: Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. https://app.cochat.ai/papers/10.1016/s1364-8152(99)00007-9
- The paper
- Maier, H. R., & Dandy, G. C. (2000). Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environmental Modelling & Software. https://doi.org/10.1016/s1364-8152(99)00007-9
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.
