Articles | Volume 25, issue 11
https://doi.org/10.5194/hess-25-5981-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-5981-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Nov 2021
Research article |
| 22 Nov 2021
| 22 Nov 2021
Design flood estimation for global river networks based on machine learning models
School of Geographical Sciences, University of Bristol, Bristol, UK
Paul Bates
School of Geographical Sciences, University of Bristol, Bristol, UK
Fathom, Engine Shed, Station Approach, Bristol, UK
Jeffrey Neal
School of Geographical Sciences, University of Bristol, Bristol, UK
Fathom, Engine Shed, Station Approach, Bristol, UK
Bo Pang
College of Water Sciences, Beijing Normal University, Beijing, China
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Cited
15 citations as recorded by crossref.
- Deep learning methods for flood mapping: a review of existing applications and future research directions R. Bentivoglio et al. 10.5194/hess-26-4345-2022
- Performance benchmarking on several regression models applied in urban flash flood risk assessment H. Hu et al. 10.1007/s11069-023-06341-y
- Applying machine learning in the investigation of the link between the high-velocity streams of charged solar particles and precipitation-induced floods S. Malinović-Milićević et al. 10.1007/s10661-024-12537-x
- Mapping global non-floodplain wetlands C. Lane et al. 10.5194/essd-15-2927-2023
- Continental Scale Regional Flood Frequency Analysis: Combining Enhanced Datasets and a Bayesian Framework D. Alexandre et al. 10.3390/hydrology11080119
- Flood Defense Standard Estimation Using Machine Learning and Its Representation in Large‐Scale Flood Hazard Modeling G. Zhao et al. 10.1029/2022WR032395
- Simulation of monthly river flow using SVR neural network improved with population-based optimization algorithms A. Kohansarbaz et al. 10.1007/s40808-024-02040-0
- Improving Bayesian Model Averaging for Ensemble Flood Modeling Using Multiple Markov Chains Monte Carlo Sampling T. Huang & V. Merwade 10.1029/2023WR034947
- HYDROLOGICAL REGIONALIZATION OF THE ANNUAL MAXIMUM STREAMFLOWS OF THE UPPER AND MIDDLE PARAOPEBA RIVER – MG USING THE INDEX-FLOOD TECHNIQUE J. Coelho Filho & M. Durães 10.1590/1809-4430-eng.agric.v42n4e20220035/2022
- A climate-conditioned catastrophe risk model for UK flooding P. Bates et al. 10.5194/nhess-23-891-2023
- Prediction of flood quantiles at ungauged catchments for the contiguous USA using Artificial Neural Networks V. Filipova et al. 10.2166/nh.2021.082
- Flood frequency analysis in the lower Burhi Dehing River in Assam, India using Gumbel Extreme Value and log Pearson Type III methods A. Handique et al. 10.1007/s44288-024-00084-4
- Recommendations to improve the interpretation of global flood forecasts to support international humanitarian operations for tropical cyclones L. Speight et al. 10.1111/jfr3.12952
- Alternate pathway for regional flood frequency analysis in data-sparse region N. Mangukiya & A. Sharma 10.1016/j.jhydrol.2024.130635
- Uncertainty in the extreme flood magnitude estimates of large-scale flood hazard models L. Devitt et al. 10.1088/1748-9326/abfac4
14 citations as recorded by crossref.
- Deep learning methods for flood mapping: a review of existing applications and future research directions R. Bentivoglio et al. 10.5194/hess-26-4345-2022
- Performance benchmarking on several regression models applied in urban flash flood risk assessment H. Hu et al. 10.1007/s11069-023-06341-y
- Applying machine learning in the investigation of the link between the high-velocity streams of charged solar particles and precipitation-induced floods S. Malinović-Milićević et al. 10.1007/s10661-024-12537-x
- Mapping global non-floodplain wetlands C. Lane et al. 10.5194/essd-15-2927-2023
- Continental Scale Regional Flood Frequency Analysis: Combining Enhanced Datasets and a Bayesian Framework D. Alexandre et al. 10.3390/hydrology11080119
- Flood Defense Standard Estimation Using Machine Learning and Its Representation in Large‐Scale Flood Hazard Modeling G. Zhao et al. 10.1029/2022WR032395
- Simulation of monthly river flow using SVR neural network improved with population-based optimization algorithms A. Kohansarbaz et al. 10.1007/s40808-024-02040-0
- Improving Bayesian Model Averaging for Ensemble Flood Modeling Using Multiple Markov Chains Monte Carlo Sampling T. Huang & V. Merwade 10.1029/2023WR034947
- HYDROLOGICAL REGIONALIZATION OF THE ANNUAL MAXIMUM STREAMFLOWS OF THE UPPER AND MIDDLE PARAOPEBA RIVER – MG USING THE INDEX-FLOOD TECHNIQUE J. Coelho Filho & M. Durães 10.1590/1809-4430-eng.agric.v42n4e20220035/2022
- A climate-conditioned catastrophe risk model for UK flooding P. Bates et al. 10.5194/nhess-23-891-2023
- Prediction of flood quantiles at ungauged catchments for the contiguous USA using Artificial Neural Networks V. Filipova et al. 10.2166/nh.2021.082
- Flood frequency analysis in the lower Burhi Dehing River in Assam, India using Gumbel Extreme Value and log Pearson Type III methods A. Handique et al. 10.1007/s44288-024-00084-4
- Recommendations to improve the interpretation of global flood forecasts to support international humanitarian operations for tropical cyclones L. Speight et al. 10.1111/jfr3.12952
- Alternate pathway for regional flood frequency analysis in data-sparse region N. Mangukiya & A. Sharma 10.1016/j.jhydrol.2024.130635
1 citations as recorded by crossref.
Latest update: 20 Nov 2024
Short summary
Design flood estimation is a fundamental task in hydrology. We propose a machine- learning-based approach to estimate design floods anywhere on the global river network. This approach shows considerable improvement over the index-flood-based method, and the average bias in estimation is less than 18 % for 10-, 20-, 50- and 100-year design floods. This approach is a valid method to estimate design floods globally, improving our prediction of flood hazard, especially in ungauged areas.
Design flood estimation is a fundamental task in hydrology. We propose a machine- learning-based...
