Linear vs. non-linear regional flood estimation models in New South Wales, Australia

This study aimed to compare linear and non-linear regional flood frequency analysis (RFFA) models where streamflow data of 88 catchments of New South Wales (NSW), Australia, were utilized. The Quantile Regression Technique (QRT) was selected as the linear model and an Artificial Neural Network (ANN) as the non-linear model. Six different flood quantiles were considered, which are annual exceedance probabilities of 1 in 2 (Q₂), 1 in 5 (Q₅), 1 in 10 (Q₁₀), 1 in 20 (Q₂₀), 1 in 50 (Q₅₀), and 1 in 100 (Q₁₀₀). The selected two RFFA models were compared using a split-sample validation technique (70% data for training and 30% data for testing) and several statistical indices like relative error (RE), absolute median relative error (RE_r), bias, the median ratio of the predicted and observed flood quantiles (Q_r), and the root mean square error (RMSE). The ANN model exhibited smaller bias values for Q₂, Q₅, Q₂₀, and Q₅₀ and smaller Q_r values for Q₁₀, Q₂₀, and Q₅₀. The REr values for the ANN model were found to be lower for smaller return periods (Q₂, Q₅, and Q₁₀). The overall RE_r value considering all six AEPs for the ANN model is 35%, which is 37% for the QRT model. The results of this study could assist to select a suitable RFFA technique for design application in the study area.