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|Title:||Rainfall runoff modelling using neural networks: state-of-the-art and future research needs|
|Citation:||ISH Journal of Hydraulic Engineering, 2009; 15(1):52-74|
|Publisher:||The Indian Society for Hydraulics|
|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|
|Appears in Collections:||Aurora harvest 5|
Civil and Environmental Engineering publications
Environment Institute publications
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