TY - JOUR
T1 - Predicting outflow hydrographs of potential dike breaches in a bifurcating river system using NARX neural networks
AU - Bomers, Anouk
N1 - Funding Information:
Funding: This research is supported by the Netherlands Organisation for Scientific Research (NWO, project 14506), which is partly funded by the Ministry of Economic Affairs and Climate Policy. Furthermore, the research is supported by the Ministry of Infrastructure and Water Management and Deltares.
Publisher Copyright:
© 2021 by the author. Licensee MDPI, Basel, Switzerland.
Financial transaction number:
342123266
PY - 2021/6/3
Y1 - 2021/6/3
N2 - 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
AB - 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
U2 - 10.3390/hydrology8020087
DO - 10.3390/hydrology8020087
M3 - Article
SN - 2306-5338
VL - 8
JO - Hydrology
JF - Hydrology
IS - 2
M1 - 87
ER -
