Bayesian network model for flood forecasting based on atmospheric ensemble forecasts

Goodarzi, Leila; Banihabib, Mohammad E.; Roozbahani, Abbas; Dietrich, Jörg

The purpose of this study is to propose the Bayesian network (BN) model to estimate flood peaks from atmospheric ensemble forecasts (AEFs). The Weather Research and Forecasting (WRF) model was used to simulate historic storms using five cumulus parameterization schemes. The BN model was trained to compute flood peak forecasts from AEFs and hydrological pre-conditions. The mean absolute relative error was calculated as 0.076 for validation data. An artificial neural network (ANN) was applied for the same problem but showed inferior performance with a mean absolute relative error of 0.39. It seems that BN is less sensitive to small data sets, thus it is more suited for flood peak forecasting than ANN.

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Goodarzi, Leila / Banihabib, Mohammad E. / Roozbahani, Abbas / et al: Bayesian network model for flood forecasting based on atmospheric ensemble forecasts. 2019. Copernicus Publications.

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Rechteinhaber: Leila Goodarzi et al.

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