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Abstract: Rainfall-Runoff models are mostly empirical in nature demanding the knowledge of a large number of catchment parameters. On the contrary Artificial Neural Networks (ANN) can be deployed in cases where t he available data is limited. The present work involves the development of an ANN model using Backward Propagation algorithm. The hydrologic variables used were Rainfall, Soil Moisture, Evaporation and Runoff of monsoon months for a specific period. Model 1 involved the training of the ANN model ... (Update)
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BibTeX entry: (Update)
@misc{ kumar-application,
author = "D. Nagesh Kumar and Abhijit Ray",
title = "Application of Artificial Neural Networks for Rainfall-Runoff Modelling",
url = "citeseer.ist.psu.edu/718551.html" }
Citations (may not include all citations):1491 Learning internal representations by error propagation (context) - Rumelhart, Hinton et al. - 1986
227 An introduction to computing with Neural nets (context) - Lippmann
4 Artificial Neural Networks: Theoretical concepts (context) - Vemuri - 1988
1 Neurocomputing: Picking the human brain (context) - Hecht-Nielsen - 1988
1 Adaptive pattern recognition & Neural netowrks (context) - Pao - 1985
1 Rainfall-Runoff modelling - Past (context) - Todini - 1988
1 Comparison of Rainfall-Runoff modelling approaches (context) - Chiew, Stewardson - 1993
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