Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/58435
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Type: Journal article
Title: Rainfall runoff modelling using neural networks: state-of-the-art and future research needs
Author: Jain, A.
Maier, H.
Dandy, G.
Sudheer, K.
Citation: ISH Journal of Hydraulic Engineering, 2009; 15(1):52-74
Publisher: The Indian Society for Hydraulics
Issue Date: 2009
ISSN: 0971-5010
2164-3040
Statement of
Responsibility: 
Ashu Jain, Holger R. Maier, G.C. Dandy and K.P. Sudheer
Abstract: Modeling of rainfall runoff (R-R) processes is useful in many water resources management activities. Traditionally, hydrologists have employed deterministic/conceptual methods for R-R modeling. Recently, Artificial Neural Networks (ANNs) have become popular tools for R-R modeling. This paper reviews the literature on and presents state-of-the-art approaches to ANN R-R modeling. Certain aspects of ANN R-R modeling have been covered in greater detail. These include input selection, data division, ANN training, hybrid modeling, and extrapolation beyond the range of training data. There is a strong need to carry out extensive research on these aspects while developing ANN R-R models. © 2009 Taylor & Francis Group, LLC.
Rights: Copyright status unknown
DOI: 10.1080/09715010.2009.10514968
Description (link): http://www.e-ish.net/JOURNALS/jMay09_special.htm
Appears in Collections:Aurora harvest 5
Civil and Environmental Engineering publications
Environment Institute publications

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