Neural networks for fast fluvial flood predictions: Too good to be true?

Accurate models with low computational times are required to predict the consequences of fluvial floods in real-time. Even though detailed hydraulic models can predict flood water levels and corresponding inundation extents with high accuracy, their computational times limit their applicability to being used as a flood early-warning system. Therefore, conceptual models were developed in the literature for many years. These types of models do not attempt to represent the complex dynamic flood generation processes but are based on simplified hydraulic concepts, generally only using a digital elevation model as input. However, a shift in this research field is currently present from conceptual models to data-driven models, and more specifically to neural networks. This paper discusses the benefits and drawbacks of both modelling approaches and speculates which method is most promising to be used as a flood warning system to predict the consequences of fluvial floods in real-time.