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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Cited
17 citations as recorded by crossref.
- Flood Image Classification using Convolutional Neural Networks O. Adetunji et al. 10.53982/ajerd.2023.0602.11-j
- The role of artificial intelligence (AI) and Chatgpt in water resources, including its potential benefits and associated challenges S. Haider et al. 10.1007/s43832-024-00173-y
- A comprehensive review of Bayesian statistics in natural hazards engineering Y. Zheng et al. 10.1007/s11069-021-04729-2
- 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. 10.1016/j.ijdrr.2020.101705
- Predictive Performance of Ensemble Learning Boosting Techniques in Daily Streamflow Simulation D. Chandran & N. Chithra 10.1007/s11269-024-04029-x
- Improving Long-Term Flood Forecasting Accuracy Using Ensemble Deep Learning Models and an Attention Mechanism M. Kordani et al. 10.1061/JHYEFF.HEENG-6262
- Daily reservoir inflow forecasting using weather forecast downscaling and rainfall-runoff modeling: Application to Urmia Lake basin, Iran A. Meydani et al. 10.1016/j.ejrh.2022.101228
- Data-driven approaches to built environment flood resilience: A scientometric and critical review P. Rathnasiri et al. 10.1016/j.aei.2023.102085
- Multi-Step Ahead Probabilistic Forecasting of Daily Streamflow Using Bayesian Deep Learning: A Multiple Case Study F. Ghobadi & D. Kang 10.3390/w14223672
- A new method to analyze the driving mechanism of flood disaster resilience and its management decision-making D. Liu et al. 10.1016/j.jhydrol.2022.128134
- Evolution of Flood Prediction and Forecasting Models for Flood Early Warning Systems: A Scoping Review N. Byaruhanga et al. 10.3390/w16131763
- Ensemble flood forecasting: Current status and future opportunities W. Wu et al. 10.1002/wat2.1432
- Know to Predict, Forecast to Warn: A Review of Flood Risk Prediction Tools K. Antwi-Agyakwa et al. 10.3390/w15030427
- Using Evolving ANN-Based Algorithm Models for Accurate Meteorological Forecasting Applications in Vietnam T. Chen et al. 10.1155/2020/8179652
- Flood susceptibility mapping using a novel integration of multi-temporal sentinel-1 data and eXtreme deep learning model R. Al-Ruzouq et al. 10.1016/j.gsf.2024.101780
- Risk Prediction by Using Artificial Neural Network in Global Software Development A. Iftikhar et al. 10.1155/2021/2922728
- Performance evaluation of artificial intelligence paradigms—artificial neural networks, fuzzy logic, and adaptive neuro-fuzzy inference system for flood prediction R. Tabbussum & A. Dar 10.1007/s11356-021-12410-1
16 citations as recorded by crossref.
- Flood Image Classification using Convolutional Neural Networks O. Adetunji et al. 10.53982/ajerd.2023.0602.11-j
- The role of artificial intelligence (AI) and Chatgpt in water resources, including its potential benefits and associated challenges S. Haider et al. 10.1007/s43832-024-00173-y
- A comprehensive review of Bayesian statistics in natural hazards engineering Y. Zheng et al. 10.1007/s11069-021-04729-2
- 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. 10.1016/j.ijdrr.2020.101705
- Predictive Performance of Ensemble Learning Boosting Techniques in Daily Streamflow Simulation D. Chandran & N. Chithra 10.1007/s11269-024-04029-x
- Improving Long-Term Flood Forecasting Accuracy Using Ensemble Deep Learning Models and an Attention Mechanism M. Kordani et al. 10.1061/JHYEFF.HEENG-6262
- Daily reservoir inflow forecasting using weather forecast downscaling and rainfall-runoff modeling: Application to Urmia Lake basin, Iran A. Meydani et al. 10.1016/j.ejrh.2022.101228
- Data-driven approaches to built environment flood resilience: A scientometric and critical review P. Rathnasiri et al. 10.1016/j.aei.2023.102085
- Multi-Step Ahead Probabilistic Forecasting of Daily Streamflow Using Bayesian Deep Learning: A Multiple Case Study F. Ghobadi & D. Kang 10.3390/w14223672
- A new method to analyze the driving mechanism of flood disaster resilience and its management decision-making D. Liu et al. 10.1016/j.jhydrol.2022.128134
- Evolution of Flood Prediction and Forecasting Models for Flood Early Warning Systems: A Scoping Review N. Byaruhanga et al. 10.3390/w16131763
- Ensemble flood forecasting: Current status and future opportunities W. Wu et al. 10.1002/wat2.1432
- Know to Predict, Forecast to Warn: A Review of Flood Risk Prediction Tools K. Antwi-Agyakwa et al. 10.3390/w15030427
- Using Evolving ANN-Based Algorithm Models for Accurate Meteorological Forecasting Applications in Vietnam T. Chen et al. 10.1155/2020/8179652
- Flood susceptibility mapping using a novel integration of multi-temporal sentinel-1 data and eXtreme deep learning model R. Al-Ruzouq et al. 10.1016/j.gsf.2024.101780
- Risk Prediction by Using Artificial Neural Network in Global Software Development A. Iftikhar et al. 10.1155/2021/2922728
Latest update: 13 Feb 2025
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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