Comparing Performance of ANN and SVM Methods for Regional Flood Frequency Analysis in South-East Australia

Design flood estimations at ungauged catchments are a challenging task in hydrology. Regional frequency analysis (RFFA) is widely used for this purpose. This paper develops artificial intelligence (AI)-based RFFA models (artificial neural networks (ANN) and support vector machine (SVM)) using data from 181 gauged South-East Australia. Based on an independent testing, it found that the ANN method outperforms SVM (the relative error values model range 33–54% as compared to 37–64% SVM). The generate more accurate quantiles smaller return periods; however, higher periods, both methods present estimation error. results of study will help recommend new AI-based