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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 |
Published version: | http://dx.doi.org/10.1080/09715010.2009.10514968 |
Appears in Collections: | Aurora harvest 5 Civil and Environmental Engineering publications Environment Institute publications |
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