Design Flood Estimation for Ungauged Catchments: Application of Artificial Neural Networks for Eastern Australia

Design flood estimation in small to medium sized ungauged catchments is frequently required in hydrological design of water infrastructure. In Australia, design flood estimation in smaller ungauged catchments is often estimated using the rational method. In recent years, there have been notable researches in Australia on the replacement of the rational method by other techniques which are hydrologically more meaningful and which can overcome the major limitations with the rational method. These methods include various forms of regression approaches and index flood methods. This paper focuses on the application of the artificial neural networks (ANN) to design flood estimation in ungauged catchments in the eastern part of Australia. This uses data from 399 stream gauging stations across eastern Australia to develop a regional flood estimation method based on the ANN. An independent test based on split-sample validation shows that the ANN can provide quite reasonable design flood estimates for small to medium sized ungauged catchments in eastern part of Australia. The best model was found to include two variables, catchment area and design rainfall intensity for the average recurrence intervals in the range of 10 to 100 years.