Fast flood modelling after dike breaches: machine learning or conceptual models?

Two-dimensional depth-averaged (2DH) hydrodynamic models are often used to obtain insight in the consequences of a flood. However, the long computation times of these models do not allow for real-time flood forecasting. Surrogate models such as machine learning models or conceptual models promise much lower computation times while achieving reasona-ble accuracy. In this study, we develop and compare a machine learning model and a conceptual model for the prediction of overland flow propagation after a dike breach. We find a high accu-racy of around 95% by the machine learning model, and a reasonable accuracy of around 70–80% by the conceptual model. We discuss trade-offs of each method regarding practical use in a real-time flood forecasting system, and conclude that both machine learning and conceptual models have a growing capability to be applied during time-sensitive emergency situations.