The dynamic and accurate flood forecasting of daily stream flow processes of a river are important in the management of extreme events such as flash floods, floods and optimal design of water storage structures and drainage network. This paper aims to recommend a best hydrologic models are linear stochastic models autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA) and nonlinear models like Artificial neural network (ANN) and Wavelet neural network (WNN) for flood forecasting of Vamsadhara river in avelet neural network (WNN) is an hybrid modelling approach for forecasting of river flow using daily time series s data of river flow into sub series with low (approximation) and high (details) frequency, and these sub series were then used as input data for the artificial neural network (ANN). WNN flow data was collected from India-WRIS . 60% data was used for model calibration and 40% for validation. The one day ahead forecasting mpared. The comparison of model forecasting performance was conducted based upon different statistical indices and graphical criteria. The result indicates that WNN model is better than ANN, ARIMA and ARMA.