LAUSR

Early flood forecasting systems can mitigate flood damage during extreme events. Typically, the effects of flood events in terms of inundation depths and extents are computed using detailed hydraulic models. However, a major drawback of these models is the computational time, which is generally in the order of hours to days for large river basins. Gaining insight in the outflow hydrographs in case of dike breaches is especially important to estimate inundation extents. In this study, NARX neural networks that were capable of predicting outflow hydrographs of multiple dike breaches accurately were developed. The timing of the dike failures and the cumulative outflow volumes were accurately predicted. These findings show that neural networks—specifically, NARX networks that are capable of predicting flood time series—have the potential to be used within a flood early warning system in the future.