From the review of applications for the artificial neural network(ANN)in hydrology,three real-time flood-forecasting schemes based on the ANN are generalized in this paper.The first scheme is the ANN model in the simulation model plus an AR model to forecast the simulation errors of the ANN model.The second one is the ANN model in the real-time mode with all the weights of the ANN being fixed.The third one is the ANN model in the real-time mode whose weights are continuingly updated by the back-propagation method.The daily data from the ten different watersheds are selected to test these three schemes in terms of their efficiency in real-time flood forecasting.It is found that the third scheme on average performs best in flood forecasting.Moreover,when compared to the first scheme,the third scheme is more parsimonious since it does not need any additional"correction mod el".It is recom-mended that the third real-time scheme be used in the flow forecasting practice.