Development of effective and efficient rainfall-runoff models using integration of deterministic, real-coded genetic algorithms and artificial neural network techniques-Reference-Cited by-同舟云学术

Development of effective and efficient rainfall-runoff models using integration of deterministic, real-coded genetic algorithms and artificial neural network techniques

Published:2004-04 Issue:4 Volume:40 Page:
ISSN:0043-1397
Container-title:Water Resources Research
language:en
Short-container-title:Water Resour. Res.

Author:

Jain Ashu¹,Srinivasulu Sanaga¹

Affiliation:

1. Department of Civil Engineering; Indian Institute of Technology; Kanpur India

Publisher

American Geophysical Union (AGU)

Subject

Water Science and Technology

Link

http://onlinelibrary.wiley.com/wol1/doi/10.1029/2003WR002355/fullpdf

Reference38 articles.

1. Performance of neural networks in daily streamflow forecasting;Birikundavyi;J. Hydrol. Eng.,2002

2. River Flood forecasting with neural network model;Campolo;Water Resour. Res.,1999

3. Chow , V. T. D. R. Maidment L. W. Mays 1988 Applied Hydrology McGraw-Hill New York

4. Neural network: A need for caution;Curry;Omega Int. J. Manage. Sci.,1997

5. An artificial neural network approach to rainfall-runoff modeling;Dawson;Hydrol. Sci. J.,1998

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