Abstract:
The current study demonstrates the potential use of wavelet neural network (WNN) for river flow modeling
by developing a rainfall-runoff model for Malaprabha basin in India. Daily data of rainfall, discharge,
and evaporation for 21 years (from 1980 to 2000) have been used for modeling. In the modeling original
model, inputs have been decomposed by wavelets and decomposed sub-series were taken as input to
ANN. Model parameters are calibrated using 17 years of data and rest of the data are used for model validation.
Statistical approach has been used to find out the model input. Optimum architectures of the
WNN models are selected according to the obtained evaluation criteria in terms of Nash–Sutcliffe efficiency
coefficient and root mean squared error. Result of this study has been compared by developing
standard neural network model and NAM model. The results of this study indicate that the WNN model
performs better compared to an ANN and NAM model in estimating the hydrograph characteristics such
as flow duration curve effectively.
