Comparison of Multilayer Perceptron and Radial Basis Function networks as tools for flood forecasting

This paper presents a comparison between two Artificial Neural Network (ANN) approaches, namely, Multilayer Perceptron (MLP) and Radial Basis Function (RBF) networks, in flood forecasting. The basic difference between the two methods is that the parameters of the former network are nonlinear and those of the latter are linear. The optimum model parameters are therefore guaranteed in the latter, whereas it is not so in the more popularly adopted former approach. The two methods are applied to predict water levels at stations in an experimental drainage basin and in a major river in China during storm periods. The RBF network based models give predictions comparable in accuracy to those from the MLP based models. It is also observed that the RBF approach requires less time for model development since no repetition is required to reach the optimum model parameters.