The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Rainfall forecasting in space and time using a neural network
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Citations
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
Cited by
Maier, H. R., & Dandy, G. C. (2000) · Environmental Modelling & Software · doi:10.1016/s1364-8152(99)00007-9 · Cited by 2,377
Artificial Neural Network Modeling of the Rainfall‐Runoff Process
Hsu, K., Gupta, H. V., & Sorooshian, S. (1995) · Water Resources Research · doi:10.1029/95wr01955 · Cited by 1,515
Hong, Y., Hsu, K.-L., Sorooshian, S., & Gao, X. (2004) · Journal of Applied Meteorology · doi:10.1175/jam2173.1 · Cited by 851
An artificial neural network approach to rainfall-runoff modelling
DAWSON, C. W., & WILBY, R. (1998) · Hydrological Sciences Journal · doi:10.1080/02626669809492102 · Cited by 695
Groundwater level forecasting using artificial neural networks
Daliakopoulos, I. N., Coulibaly, P., & Tsanis, I. K. (2005) · Journal of Hydrology · doi:10.1016/j.jhydrol.2004.12.001 · Cited by 664
Metadata
- Authors
- French, M. N., Krajewski, W. F., & Cuykendall, R. R.
- Venue
- Journal of Hydrology
- Publisher
- Elsevier BV
- Published
- 1992-08
- Volume
- 137
- Issue
- 1-4
- Pages
- 1-31
- ISSN
- 0022-1694 (ISSN-L)
- OpenAlex
- W1996150433
- Cited by
- 692
- References
- 2
- FWCI
- 4.70
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Rainfall forecasting in space and time using a neural network. https://app.cochat.ai/papers/10.1016/0022-1694(92)90046-x
- The paper
- French, M. N., Krajewski, W. F., & Cuykendall, R. R. (1992). Rainfall forecasting in space and time using a neural network. Journal of Hydrology. https://doi.org/10.1016/0022-1694(92)90046-x
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.
