Details
| Original language | English |
|---|---|
| Pages (from-to) | 743-754 |
| Number of pages | 12 |
| Journal | Journal of hydrology |
| Volume | 575 |
| Early online date | 22 May 2019 |
| Publication status | Published - Aug 2019 |
Abstract
The real-time forecasting of urban flooding is a challenging task for the following two reasons: (1) urban flooding is often characterized by short lead times, (2) the uncertainty in precipitation forecasting is usually high. Standard physically based numerical models are often too slow for the use in real-time forecasting systems. Data driven models have small computational costs and fast computation times and may be useful to overcome this problem. The present study presents an artificial neural network based model for the prediction of maximum water levels during a flash flood event. The challenge of finding a suitable structure for the neural network was solved with a new growing algorithm. The model is successfully tested for spatially uniformly distributed synthetic rain events in two real but slightly modified urban catchments with different surface slopes. The computation time of the model in the order of seconds and the accuracy of the results are convincing, which suggest that the method may be useful for real-time forecasts.
Keywords
- Artificial neural network, Ensemble neural network, Real-time forecast, Urban flooding
ASJC Scopus subject areas
- Environmental Science(all)
- Water Science and Technology
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In: Journal of hydrology, Vol. 575, 08.2019, p. 743-754.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - An ensemble neural network model for real-time prediction of urban floods
AU - Berkhahn, Simon
AU - Fuchs, Lothar
AU - Neuweiler, Insa
N1 - Funding information: We thank Aaron Peche, Robert Sämann and Tim Berthold for discussion and technical support. Furthermore, we thank the anonymous reviewers and Slobodan Djordjevic for challenging comments which helped to improve the present study. The research is being conducted with financial support of the state of Lower Saxony and the BMBF funded research project EVUS (EVUS – Real-Time Prediction of Pluvial Floods and Induced Water Contamination in Urban Areas) [BMBF, 03G0846A].
PY - 2019/8
Y1 - 2019/8
N2 - The real-time forecasting of urban flooding is a challenging task for the following two reasons: (1) urban flooding is often characterized by short lead times, (2) the uncertainty in precipitation forecasting is usually high. Standard physically based numerical models are often too slow for the use in real-time forecasting systems. Data driven models have small computational costs and fast computation times and may be useful to overcome this problem. The present study presents an artificial neural network based model for the prediction of maximum water levels during a flash flood event. The challenge of finding a suitable structure for the neural network was solved with a new growing algorithm. The model is successfully tested for spatially uniformly distributed synthetic rain events in two real but slightly modified urban catchments with different surface slopes. The computation time of the model in the order of seconds and the accuracy of the results are convincing, which suggest that the method may be useful for real-time forecasts.
AB - The real-time forecasting of urban flooding is a challenging task for the following two reasons: (1) urban flooding is often characterized by short lead times, (2) the uncertainty in precipitation forecasting is usually high. Standard physically based numerical models are often too slow for the use in real-time forecasting systems. Data driven models have small computational costs and fast computation times and may be useful to overcome this problem. The present study presents an artificial neural network based model for the prediction of maximum water levels during a flash flood event. The challenge of finding a suitable structure for the neural network was solved with a new growing algorithm. The model is successfully tested for spatially uniformly distributed synthetic rain events in two real but slightly modified urban catchments with different surface slopes. The computation time of the model in the order of seconds and the accuracy of the results are convincing, which suggest that the method may be useful for real-time forecasts.
KW - Artificial neural network
KW - Ensemble neural network
KW - Real-time forecast
KW - Urban flooding
UR - http://www.scopus.com/inward/record.url?scp=85067078458&partnerID=8YFLogxK
U2 - 10.15488/12043
DO - 10.15488/12043
M3 - Article
AN - SCOPUS:85067078458
VL - 575
SP - 743
EP - 754
JO - Journal of hydrology
JF - Journal of hydrology
SN - 0022-1694
ER -