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A data‐driven algorithm for constructing artificial neural network rainfall‐runoff models
Abstract
A new approach for designing the network structure in an artificial neural network (ANN)‐based rainfall‐runoff model is presented. The method utilizes the statistical properties such as cross‐, auto‐ and partial‐auto‐correlation of the data series in identifying a unique input vector that best represents the process for the basin, and a standard algorithm for training. The methodology has been validated using the data for a river basin in India. The results of the study are highly promising and indicate that it could significantly reduce the effort and computational time required in developing an ANN model. Copyright © 2002 John Wiley & Sons, Ltd.
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Citations
Cited by
Sensitivity and identifiability of stream flow generation parameters of the SWAT model
Cibin, R., Sudheer, K. P., & Chaubey, I. (2010) · Hydrological Processes · doi:10.1002/hyp.7568 · Cited by 309
Self-citationApplication of support vector regression for modeling low flow time series
Sahoo, B. B., Jha, R., Singh, A., & Kumar, D. (2019) · KSCE Journal of Civil Engineering · doi:10.1007/s12205-018-0128-1 · Cited by 56
Metadata
- Authors
- Sudheer, K. P., Gosain, A. K., & Ramasastri, K. S.
- Venue
- Hydrological Processes
- Publisher
- Wiley
- Published
- 2002-03-15
- Volume
- 16
- Issue
- 6
- Pages
- 1325-1330
- ISSN
- 0885-6087 (ISSN-L)
- 1099-1085
- OpenAlex
- W2009203913
- Cited by
- 418
- References
- 0
- FWCI
- 11.40
- Type
- journal article
- Language
- en
- Open access
- closed
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- Sudheer, K. P., Gosain, A. K., & Ramasastri, K. S. (2002). A data‐driven algorithm for constructing artificial neural network rainfall‐runoff models. Hydrological Processes. https://doi.org/10.1002/hyp.554
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