Lumped and distributed data ANN models for an intermittent river
DSpace at IIT Bombay
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Title |
Lumped and distributed data ANN models for an intermittent river
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Creator |
MAGAR, R.B
SUNIL, K JOTHIPRAKASH, V |
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Subject |
Hydrologic models, Rainfall-runoff relationship, Artificial neural network, Lumped data, Distributed data, Intermittent river.
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Description |
This study presents the application of Artificial Neural Network (ANN) models for intermittent rainfall-runoff processes using lumped and distributed data. The developed Multilayer Perceptron (MLP) ANN models are trained with Back propagation (BP), Conjugate Gradient (CG), and Lavenberg-Marquardt (LM) algorithm. All the networks are trained with various combinations of length of data, structure, activation function, momentum factor, learning rate and number of epochs. Since the rainfall-runoff cross correlation is good, both lumped and distributed data ANN models performed equally better. However, from the cause effective ANN models it has been found that the BP algorithm performed well for lumped data and CG algorithm performed well for distributed data. It has also been found that the distributed model performed better in mapping the rainfall-runoff relationship for an intermittent river system.
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Publisher |
Serialpublication
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Date |
2012-07-17T10:24:34Z
2012-07-17T10:24:34Z 2010 |
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Type |
Article
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Identifier |
Journal of Flood Engineering,1(2)159-173
0976-6219 http://dspace.library.iitb.ac.in/jspui/handle/100/14387 |
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Language |
English
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