Articles | Volume 19, issue 11
https://doi.org/10.5194/nhess-19-2513-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-19-2513-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Nov 2019
Research article |
| 13 Nov 2019
| 13 Nov 2019
Bayesian network model for flood forecasting based on atmospheric ensemble forecasts
Leila Goodarzi
Department of Irrigation and Drainage, College of Aburaihan, University of Tehran, Tehran, Iran
Mohammad E. Banihabib
Department of Irrigation and Drainage, College of Aburaihan, University of Tehran, Tehran, Iran
Abbas Roozbahani
Department of Irrigation and Drainage, College of Aburaihan, University of Tehran, Tehran, Iran
Jörg Dietrich
CORRESPONDING AUTHOR
Institute of Hydrology and Water Resources Management, Leibniz
University Hannover, Hanover, Germany
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Total article views: 2,988 (including HTML, PDF, and XML)
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Total article views: 927 (including HTML, PDF, and XML)
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Cited
19 citations as recorded by crossref.
- Flood Image Classification using Convolutional Neural Networks O. Adetunji et al.
- The role of artificial intelligence (AI) and Chatgpt in water resources, including its potential benefits and associated challenges S. Haider et al.
- A comprehensive review of Bayesian statistics in natural hazards engineering Y. Zheng et al.
- Earthquake risk assessment using an integrated Fuzzy Analytic Hierarchy Process with Artificial Neural Networks based on GIS: A case study of Sanandaj in Iran P. Yariyan et al.
- Predictive Performance of Ensemble Learning Boosting Techniques in Daily Streamflow Simulation D. Chandran & N. Chithra
- Impact-Based Decision Making in Flood Risk Management: A Review of Behavioral and Technical Approaches Z. Bovermann et al.
- A hybrid modelling framework for zero-inflated and skewed streamflow: enhancing prediction of extreme flows in tropical rain fed catchments D. Chandran & N. Chithra
- Improving Long-Term Flood Forecasting Accuracy Using Ensemble Deep Learning Models and an Attention Mechanism M. Kordani et al.
- Daily reservoir inflow forecasting using weather forecast downscaling and rainfall-runoff modeling: Application to Urmia Lake basin, Iran A. Meydani et al.
- Data-driven approaches to built environment flood resilience: A scientometric and critical review P. Rathnasiri et al.
- Multi-Step Ahead Probabilistic Forecasting of Daily Streamflow Using Bayesian Deep Learning: A Multiple Case Study F. Ghobadi & D. Kang
- A new method to analyze the driving mechanism of flood disaster resilience and its management decision-making D. Liu et al.
- Evolution of Flood Prediction and Forecasting Models for Flood Early Warning Systems: A Scoping Review N. Byaruhanga et al.
- Modeling urban flood susceptibility and identifying key flood-inducing factor chains using Bayesian network W. Zhu et al.
- Ensemble flood forecasting: Current status and future opportunities W. Wu et al.
- Know to Predict, Forecast to Warn: A Review of Flood Risk Prediction Tools K. Antwi-Agyakwa et al.
- Using Evolving ANN-Based Algorithm Models for Accurate Meteorological Forecasting Applications in Vietnam T. Chen et al.
- Flood susceptibility mapping using a novel integration of multi-temporal sentinel-1 data and eXtreme deep learning model R. Al-Ruzouq et al.
- Risk Prediction by Using Artificial Neural Network in Global Software Development A. Iftikhar et al.
19 citations as recorded by crossref.
- Flood Image Classification using Convolutional Neural Networks O. Adetunji et al.
- The role of artificial intelligence (AI) and Chatgpt in water resources, including its potential benefits and associated challenges S. Haider et al.
- A comprehensive review of Bayesian statistics in natural hazards engineering Y. Zheng et al.
- Earthquake risk assessment using an integrated Fuzzy Analytic Hierarchy Process with Artificial Neural Networks based on GIS: A case study of Sanandaj in Iran P. Yariyan et al.
- Predictive Performance of Ensemble Learning Boosting Techniques in Daily Streamflow Simulation D. Chandran & N. Chithra
- Impact-Based Decision Making in Flood Risk Management: A Review of Behavioral and Technical Approaches Z. Bovermann et al.
- A hybrid modelling framework for zero-inflated and skewed streamflow: enhancing prediction of extreme flows in tropical rain fed catchments D. Chandran & N. Chithra
- Improving Long-Term Flood Forecasting Accuracy Using Ensemble Deep Learning Models and an Attention Mechanism M. Kordani et al.
- Daily reservoir inflow forecasting using weather forecast downscaling and rainfall-runoff modeling: Application to Urmia Lake basin, Iran A. Meydani et al.
- Data-driven approaches to built environment flood resilience: A scientometric and critical review P. Rathnasiri et al.
- Multi-Step Ahead Probabilistic Forecasting of Daily Streamflow Using Bayesian Deep Learning: A Multiple Case Study F. Ghobadi & D. Kang
- A new method to analyze the driving mechanism of flood disaster resilience and its management decision-making D. Liu et al.
- Evolution of Flood Prediction and Forecasting Models for Flood Early Warning Systems: A Scoping Review N. Byaruhanga et al.
- Modeling urban flood susceptibility and identifying key flood-inducing factor chains using Bayesian network W. Zhu et al.
- Ensemble flood forecasting: Current status and future opportunities W. Wu et al.
- Know to Predict, Forecast to Warn: A Review of Flood Risk Prediction Tools K. Antwi-Agyakwa et al.
- Using Evolving ANN-Based Algorithm Models for Accurate Meteorological Forecasting Applications in Vietnam T. Chen et al.
- Flood susceptibility mapping using a novel integration of multi-temporal sentinel-1 data and eXtreme deep learning model R. Al-Ruzouq et al.
- Risk Prediction by Using Artificial Neural Network in Global Software Development A. Iftikhar et al.
Saved (final revised paper)
Latest update: 06 May 2026
Short summary
We developed a novel approach in using Bayesian networks (BNs) 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. Under the limited amount of data, BN outperformed artificial neural networks with good results. Future work will validate the concept further.
We developed a novel approach in using Bayesian networks (BNs) for ensemble flood forecasting in...
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