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Counterpropagation fuzzy–neural network for streamflow reconstruction
Author(s) -
Chang FiJohn,
Hu HsiangFan,
Chen YenChang
Publication year - 2001
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.102
Subject(s) - streamflow , artificial neural network , reliability (semiconductor) , computer science , fuzzy logic , set (abstract data type) , data mining , artificial intelligence , data set , watershed , machine learning , cartography , geography , drainage basin , power (physics) , physics , quantum mechanics , programming language
Abstract This study combines neural networks and fuzzy arithmetic to present a counterpropagation fuzzy–neural network (CFNN) for streamflow reconstruction. The CFNN has a rule‐based control, a modified self‐organizing counterpropagation network, and a fuzzy control predictor. It can generate rules automatically by increasing the training data to improve the accuracy of streamflow reconstruction. The CFNN establishes the input and output relationship of a watershed without set‐up parameters. The parameters are estimated systematically by the approach converging to an optimal solution. One sequence of data generated by the Monte Carlo method is used to demonstrate the accuracy of the CFNN. The streamflow data of the Da‐chia River, in central Taiwan, is also used to evaluate the performances of the CFNN. The results indicate the reliability and accuracy of the CFNN for streamflow reconstruction. Copyright © 2001 John Wiley & Sons, Ltd.