An ensemble neural network model for real-time prediction of urban floods

Research output: Contribution to journalArticleResearchpeer review

Authors

  • Simon Berkhahn
  • Lothar Fuchs
  • Insa Neuweiler

External Research Organisations

  • Institute for Technical and Scientific Hydrology (itwh) GmbH
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Details

Original languageEnglish
Pages (from-to)743-754
Number of pages12
JournalJournal of hydrology
Volume575
Early online date22 May 2019
Publication statusPublished - 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

Cite this

An ensemble neural network model for real-time prediction of urban floods. / Berkhahn, Simon; Fuchs, Lothar; Neuweiler, Insa.
In: Journal of hydrology, Vol. 575, 08.2019, p. 743-754.

Research output: Contribution to journalArticleResearchpeer review

Berkhahn S, Fuchs L, Neuweiler I. An ensemble neural network model for real-time prediction of urban floods. Journal of hydrology. 2019 Aug;575:743-754. Epub 2019 May 22. doi: 10.15488/12043, 10.1016/j.jhydrol.2019.05.066
Berkhahn, Simon ; Fuchs, Lothar ; Neuweiler, Insa. / An ensemble neural network model for real-time prediction of urban floods. In: Journal of hydrology. 2019 ; Vol. 575. pp. 743-754.
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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.",
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