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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/nhess-2019-44
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-2019-44
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 25 Feb 2019

Research article | 25 Feb 2019

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This discussion paper is a preprint. It is a manuscript under review for the journal Natural Hazards and Earth System Sciences (NHESS).

Bayesian Network Model for Flood Forecasting Based on Atmospheric Ensemble Forecasts

Leila Goodarzi1, Mohammad Ebrahim Banihabib1, Abbas Roozbahani1, and Jörg Dietrich2 Leila Goodarzi et al.
  • 1Dept. of Irrigation and Drainage, College of Aburaihan, University of Tehran, Tehran, Iran
  • 2Institute of Hydrology and Water Resources Management, Leibniz Universität Hannover, Hannover, Germany

Abstract. The purpose of this study is to propose the Bayesian Network (BN) model to estimate flood peak from Atmospheric Ensemble Forecasts (AEFs). The Weather Research and Forecasting model was used to simulate historic storms using five cumulus parameterization schemes. The BN model was trained to forecast flood peak from AEFs. Mean Absolute Relative Error was calculated as 0.076 for validation data while it was calculated as 0.39 in artificial neural network (ANN) as a widely used model. It seems that BN is less sensitive to small data set, thus it is more suited for forecasting flood peak than ANN.

Leila Goodarzi et al.
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Leila Goodarzi et al.
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Latest update: 18 Mar 2019
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Short summary
We developed a novel approach in using Bayesian Networks (BN) for ensemble flood forecasting in a case study in Iran. This allows fast early warning without the need for hydrological modelling. We recommend to combine precipitation ensembles with hydrological initial conditions in the BN. The number of observed flood events is low by nature for training data based methods. BN outperformed Artificial Neural Networks with good results. Future work will validate the concept in other basins.
We developed a novel approach in using Bayesian Networks (BN) for ensemble flood forecasting in...
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