Regional flood frequency analysis at ungauged sites using the adaptive neuro-fuzzy inference system

In this paper, the methodology of using adaptive neuro-fuzzy inference systems (ANFIS) for flood quantile estimation at ungauged sites is presented. The proposed approach has the system identification and interpretability of fuzzy models and the learning capability of artificial neural networks (ANNs). The structure of the ANFIS is identified using the subtractive clustering algorithm. A hybrid learning algorithm consisting of back-propagation and least-squares estimation is used for system training. The ANFIS approach provides an integrated mechanism for identifying the hydrological regions, generating knowledge from the data, providing flood estimates and self-tuning to achieve the optimal performance. The proposed approach is applied to 151 catchments in the province of Quebec, Canada, and is compared to the ANN approach, the nonlinear regression (NLR) approach and the nonlinear regression with regionalization approach (NLR-R). A jackknife procedure is used for the evaluation of the performances of the three approaches. Results indicate that the ANFIS approach has a much better generalization capability than the NLR and NLR-R approaches and is comparable to the ANN approach.