Wavelet neural network model for river flow time series

Abstract

A new hybrid model that combines wavelets and an artificial neural network (ANN) called the wavelet neural network (WNN) model is proposed and applied for time series modelling of river flow. Time series of daily river flow of the Malaprabha River basin (Karnataka state, India) were analysed by the WNN model. The observed time series are decomposed into sub-series using a discrete wavelet transform and then an appropriate sub-series is used as input to the neural network for forecasting hydrological variables. The hybrid model (WNN) was compared with the standard ANN and auto-regressive (AR) models. The WNN model was able to provide a good fit with the observed data, especially the peak values during testing. The benchmark results from WNN model applications show that the hybrid model produces better results than the ANN and AR models in estimating hydrograph properties.