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.

水动力模型通常用于了解洪水风险并为洪水管理决策提供依据。但是，它们的高计算成本可能会对实时洪水预报和不确定性分析施加实际限制，而实时洪水预报和不确定性分析需要快速的模型响应或许多模型运行。仿真模型有可能减少仿真时间，同时仍保持可接受的估计精度。在这项研究中，我们提出了一种基于人工神经网络（ANN）的洪水淹没建模仿真建模框架。我们使用澳大利亚昆士兰州的河段调查了人工神经网络作为洪水淹没模型的适用性。我们的结果表明，人工神经网络可以对洪水淹没的时间序列行为进行建模，并可以大大减少所需的模拟时间，这有助于在需要快速模型响应或大量模型运行的应用中使用它们。根据模型的开发过程和结果，讨论了主要挑战和未来的研究方向。