The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Forecasting daily streamflow using hybrid ANN models
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Citations
Cites
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
River flow forecasting through conceptual models part I — A discussion of principles
Nash, J. E., & Sutcliffe, J. V. (1970) · Journal of Hydrology · doi:10.1016/0022-1694(70)90255-6 · Cited by 23,674
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
Detecting strange attractors in turbulence
Takens, F. (1981) · Lecture Notes in Mathematics · doi:10.1007/bfb0091924 · Cited by 10,459 · cited 2×
“Packard et al. (1980) and Takens (1981) proposed the time delay coordinate method to reconstruct the state space from a scalar time series.”
MethodPattern Recognition with Fuzzy Objective Function Algorithms
Bezdek, J. C. (1981) · doi:10.1007/978-1-4757-0450-1 · Cited by 9,881
Multilayer feedforward networks are universal approximators
HornikK., StinchcombeM., & WhiteH. (1989) · Neural Networks · doi:10.5555/70405.70408 · Cited by 9,081
Adaptive Mixtures of Local Experts
Jacobs, R. A., Jordan, M. I., Nowlan, S. J., & Hinton, G. E. (1991) · Neural Computation · doi:10.1162/neco.1991.3.1.79 · Cited by 5,384
Evaluating the use of “goodness‐of‐fit” Measures in hydrologic and hydroclimatic model validation
Legates, D. R., & McCabe, G. J. (1999) · Water Resources Research · doi:10.1029/1998wr900018 · Cited by 4,591
Packard, N. H., Crutchfield, J. P., Farmer, J. D., & Shaw, R. S. (1980) · Physical Review Letters · doi:10.1103/physrevlett.45.712 · Cited by 3,869
Determining embedding dimension for phase-space reconstruction using a geometrical construction
Kennel, M. B., Brown, R., & Abarbanel, H. D. I. (1992) · Physical Review A · doi:10.1103/physreva.45.3403 · Cited by 3,776
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
Rainfall–runoff modeling using artificial neural network coupled with singular spectrum analysis
Wu, C. L., & Chau, K. W. (2011) · Journal of Hydrology · doi:10.1016/j.jhydrol.2011.01.017 · Cited by 360
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
Prediction of rainfall time series using modular soft computingmethods
Wu, C. L., & Chau, K. W. (2013) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2012.05.023 · Cited by 310
Tongal, H., & Booij, M. J. (2018) · Journal of Hydrology · doi:10.1016/j.jhydrol.2018.07.004 · Cited by 267
Metadata
- Authors
- Wang, W., Gelder, P. H. A. J. M. V., Vrijling, J. K., & Ma, J.
- Venue
- Journal of Hydrology
- Publisher
- Elsevier BV
- Published
- 2006-06
- Volume
- 324
- Issue
- 1-4
- Pages
- 383-399
- ISSN
- 0022-1694 (ISSN-L)
- OpenAlex
- W2010551882
- Cited by
- 279
- References
- 38
- FWCI
- 6.84
- Type
- journal article
- Language
- en
- Open access
- closed
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- The paper
- Wang, W., Gelder, P. H. A. J. M. V., Vrijling, J. K., & Ma, J. (2006). Forecasting daily streamflow using hybrid ANN models. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2005.09.032
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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.
