Details
| Original language | English |
|---|---|
| Pages (from-to) | 2513-2524 |
| Number of pages | 12 |
| Journal | Natural Hazards and Earth System Sciences |
| Volume | 19 |
| Issue number | 11 |
| Early online date | 13 Nov 2019 |
| Publication status | E-pub ahead of print - 13 Nov 2019 |
Abstract
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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In: Natural Hazards and Earth System Sciences, Vol. 19, No. 11, 13.11.2019, p. 2513-2524.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Bayesian network model for flood forecasting based on atmospheric ensemble forecasts
AU - Goodarzi, Leila
AU - Banihabib, Mohammad E.
AU - Roozbahani, Abbas
AU - Dietrich, Jörg
N1 - Funding information: The publication of this article was funded by the open-access fund of Leibniz Universität Hannover. Financial support. The University of Tehran provided financial support for the first author during her sabbatical.
PY - 2019/11/13
Y1 - 2019/11/13
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85075074078&partnerID=8YFLogxK
U2 - 10.15488/8813
DO - 10.15488/8813
M3 - Article
AN - SCOPUS:85075074078
VL - 19
SP - 2513
EP - 2524
JO - Natural Hazards and Earth System Sciences
JF - Natural Hazards and Earth System Sciences
SN - 1561-8633
IS - 11
ER -