Abstract
AbstractThe uncertainty in flood frequency relations can be decreased by adding reconstructed historic flood events to the data set of measured annual maximum discharges. This study shows that an artificial neural network trained with a 1‐D/2‐D coupled hydraulic model is capable of reconstructing river floods with multiple dike breaches and inundations of the hinterland with high accuracy. The benefit of an artificial neural network is that it reduces computational times. With this network, the maximum discharge of the 1809 flood event of the Rhine River and its 95% confidence interval was reconstructed. The study shows that the trained artificial neural network is capable of reproducing the behavior of the hydraulic model correctly. The maximum discharge during the flood event was predicted with high accuracy even though the underlying input data are, due to the fact that the event occurred more than 200 years ago, uncertain. The confidence interval of the prediction was reduced by 43% compared to earlier predictions that did not use hydraulic models.
Highlights
River floods affect more people worldwide than any other natural hazard as they cause large economic damage and human casualties (Blöschl et al, 2017)
This study shows that an artificial neural network trained with a 1-D/2-D coupled hydraulic model is capable of reconstructing river floods with multiple dike breaches and inundations of the hinterland with high accuracy
It was found that an artificial neural network (ANN) with one hidden layer and two neurons is capable of reproducing the input-output relations of the 1-D/2-D coupled model with high accuracy
Summary
River floods affect more people worldwide than any other natural hazard as they cause large economic damage and human casualties (Blöschl et al, 2017). Flood frequency analyses are used to establish such a discharge frequency relation. This relation is computed using measured discharges. As a result of the limited data set of measured discharges and because extreme events from preinstrumental records are not included, the discharge frequency relation has a large uncertainty interval. This accounts for discharges corresponding to rare events where extrapolation is required. The uncertainty interval of the relation can be reduced by extending the data set of measured discharges with historic flood events (Bomers et al, 2019a)
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