资源环境科技发展态势分析平台

The 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.