An ANN-based emulation modelling framework for flood inundation modelling: Application, challenges and future directions. (February 2020) - Chu, Haibo; Wu, Wenyan; Wang, Q.J.; Nathan, Rory; Wei, Jiahua
Abstract: Hydrodynamic models are commonly used to understand flood risk and inform flood management decisions. However, their high computational cost can impose practical limits on real-time flood forecasting and uncertainty analysis which require fast modelling response or many model runs. Emulation models have the potential to reduce simulation times while still maintaining acceptable accuracy of the estimates. In this study, we propose an artificial neural networks (ANNs) based emulation modelling framework for flood inundation modelling. We investigate the suitability of ANNs as flood inundation models using a river segment in Queensland, Australia. Our results show that ANNs can model the time series behaviour of flood inundation and significantly reduce the simulation times required, which facilitates their use in applications requiring fast model response or a large number of model runs. Based the model development process and results, the major challenges and future research directions are discussed. Highlights: An artificial neural networks (ANN) based emulation modelling framework was developed for flood inundation modelling. Suitability of ANNs for flood inundation modelling was investigated using a real-world system. Challenges and future research on applying ANNs to simulate flood inundation are discussed.