Comparison of artificial neural networks and adaptive neuro-fuzzy inference system for regional flood estimation in Australia

Regional flood frequency analysis (RFFA) involves transfer of flood characteristics from gauged to ungauged catchments. In Australia, RFFA methods generally focus on the application of empirical methods based on linear forms of models such as the Probabilistic Rational Method, the Index Flood Method and the Quantile Regression Technique (QRT). There have been successful applications of non-linear models in RFFA in some other countries such as Artificial Neural Networks (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS). The application of these non-linear RFFA methods in Australia is limited. This study focuses on the application of ANN and ANFIS-based RFFA models to Australian data. Using data from 452 catchments in eastern Australia (a part of Australian Rainfall and Runoff Revision Project 5 regional flood methods database), it has been found that the ANN-based RFFA performs better that the ANFIS and provides quite accurate regional flood quantile estimates. However, the Bayesian Generalised Least Squares based QRT coupled with the Region of Influence approach outperforms both the ANN and ANFIS based RFFA models.