In this study, the BMA (Bayesian Model Averaging) method is used to deal with forecasts derived from three different flood routing models, i.e. the Muskingum, the one-dimensional hydrodynamic routing model and the BPNN (Back Propagation Neural Network). The numerical experiments are processed at the Wujiadu hydrological station in the Huaihe River Basin as a test site. By studying the BMA method parameters and ensemble forecast results the applicability of each method in the Huaihe River station flow forecast is verified and analyzed. According to the results from our experimental tests among 19 flood events, from 2003 to 2016, it is concluded that the BMA ensemble forecasting method can effectively avoid the flood forecast error amplification caused by uncertainty underlying in model selection and can provide high accuracy and robust flood forecasting result. After comparing the statistically optimal frequency and BMA weight value of each model, it is concluded that the BMA weight parameter reflects the probability that the model is optimal in the mean sense and is not suitable for evaluating the technicity of the model in one single flood event. It is an effective method to guide the model selection, reduce the uncertainties of flood forecasting and improve the flood forecasting techniques.