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A data‐driven algorithm for constructing artificial neural network rainfall‐runoff models
Author(s) -
Sudheer K. P.,
Gosain A. K.,
Ramasastri K. S.
Publication year - 2002
Publication title -
hydrological processes
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.222
H-Index - 161
eISSN - 1099-1085
pISSN - 0885-6087
DOI - 10.1002/hyp.554
Subject(s) - artificial neural network , surface runoff , computer science , algorithm , series (stratigraphy) , process (computing) , data mining , time series , hydrological modelling , hydrology (agriculture) , artificial intelligence , machine learning , geology , climatology , ecology , paleontology , geotechnical engineering , biology , operating system
A new approach for designing the network structure in an artificial neural network (ANN)‐based rainfall‐runoff model is presented. The method utilizes the statistical properties such as cross‐, auto‐ and partial‐auto‐correlation of the data series in identifying a unique input vector that best represents the process for the basin, and a standard algorithm for training. The methodology has been validated using the data for a river basin in India. The results of the study are highly promising and indicate that it could significantly reduce the effort and computational time required in developing an ANN model. Copyright © 2002 John Wiley & Sons, Ltd.