Co-Active 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 model such as the Probabilistic Rational Method, the Index Flood Method and the regression-based techniques. There have been successful applications of non-linear models in RFFA in some other countries such as CoActive Neuro Fuzzy Inference System (CANFIS), Gene-Expression Programming (GEP) and Artificial Neural Network (ANN). The application of these non-linear RFFA methods in Australia is limited. This study focuses on the application of Co-Active Neuro Fuzzy Inference System (CANFIS) 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 CANFIS based RFFA provides quite accurate regional flood quantile estimates. However, the Bayesian generalised least squares based QRT coupled with the region of influence approach outperforms the CANFIS based RFFA models.