Comparative study of neural network,fuzzy logic and linear transfer function techniques in daily rainfall‐runoff modelling under different input domains |
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| Authors: | Anil Kumar Lohani N. K. Goel K. K. S. Bhatia |
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| Affiliation: | 1. Scientist E1, National Institute of Hydrology, Jal Vigyan Bhawan, Roorkee‐247667, India;2. Professor, Department of Hydrology, Indian Institute of Technology, Roorkee‐247667, India;3. Director, Vira College of Engineering, Delhi Road, BIJNOR (UP), India |
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| 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. Copyright © 2010 John Wiley & Sons, Ltd. |
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| Keywords: | neural network fuzzy logic linear transfer function antecedent moisture content clustering Gaussian membership function |
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