Abstract:
The current study employs a hierarchical adaptive network-based fuzzy inference system for flood forecasting by developing a rainfall–runoff model for
the Narmada basin in India. A hybrid learning algorithm, which combines the least-square method and a back propagation algorithm, is used to identify the
parameters of the network. A subtractive clustering algorithm is used for input space partitioning in the fuzzy and neurofuzzy models. The model architectures
are trained incrementally each time step and differentmodels are developed to predict one-step andmulti-step ahead forecasts. The number of input
variables is determined using a standard statistical method. An artificial neural network (ANN) model which uses an Levenberg–Marquardt (LM) backpropagation
training algorithm has been developed for the same basin. The results of this study indicate that the hierarchical neurofuzzy model performs
better compared to an ANN and the standard fuzzy model in estimating hydrograph characteristics, especially at longer forecast time horizons.
