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Title: Comparative study of neural network, fuzzy logic and linear transfer function techniques in daily rainfall-runoff modelling under different input domains
Authors: Lohani A.K.
Goel, Narendra Kumar
Bhatia K.K.S.
Published in: Hydrological Processes
Abstract: This paper compares artificial neural network (ANN), fuzzy logic (FL) and linear transfer function (LTF)-based approaches for daily rainfall-runoff modelling. This study also investigates the potential of Takagi-Sugeno (TS) fuzzy model and the impact of antecedent soil moisture conditions in the performance of the daily rainfall-runoff models. Eleven different input vectors under four classes, i.e. (i) rainfall, (ii) rainfall and antecedent moisture content, (iii) rainfall and runoff and (iv) rainfall, runoff and antecedent moisture content are considered for examining the effects of input data vector on rainfall-runoff modelling. Using the rainfall-runoff data of the upper Narmada basin, Central India, a suitable modelling technique with appropriate model input structure is suggested on the basis of various model performance indices. The results show that the fuzzy modelling approach is uniformly outperforming the LTF and also always superior to the ANN-based models. © 2010 John Wiley & Sons,Ltd.
Citation: Hydrological Processes (2011), 25(2): 175-193
Issue Date: 2011
Keywords: Antecedent moisture content
Fuzzy logic
Gaussian membership function
Linear transfer function
Neural network
ISSN: 8856087
Author Scopus IDs: 6602080269
Author Affiliations: Lohani, A.K., Scientist E1, National Institute of Hydrology, Jal Vigyan Bhawan, Roorkee-247667, India
Goel, N.K., Professor, Department of Hydrology, Indian Institute of Technology, Roorkee-247667, India
Bhatia, K.K.S., Director, Vira College of Engineering, Delhi Road, BIJNOR (UP), India
Corresponding Author: Lohani, A.K.; Scientist E1, National Institute of Hydrology, Jal Vigyan Bhawan, Roorkee-247667, India; email:
Appears in Collections:Journal Publications [HY]

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