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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34 citations as recorded by crossref.
- Metaheuristic-optimized SVR models for daily streamflow forecasting in the Karkheh River Basin, Iran R. Dehghani & R. Chamanpira https://doi.org/10.1016/j.dwt.2025.101446
- Integrating historical archives and geospatial data to revise flood estimation equations for Philippine rivers T. Hoey et al. https://doi.org/10.5194/hess-29-6181-2025
- Climate adaptation-aware flood prediction for coastal cities using Deep Learning B. Hassan et al. https://doi.org/10.5194/hess-30-1333-2026
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- Flood Defense Standard Estimation Using Machine Learning and Its Representation in Large‐Scale Flood Hazard Modeling G. Zhao et al. https://doi.org/10.1029/2022WR032395
- Machine learning based design flood forecasting for the Ribb embankment dam in the Tana sub-basin, Abay River Basin, Ethiopia A. Belay et al. https://doi.org/10.1007/s44274-025-00372-x
- Remote sensing and the new global river science D. Feng et al. https://doi.org/10.1038/s44221-026-00665-2
- Optimizing flood resilience in China’s mountainous areas: Design flood estimation using advanced machine learning techniques X. Wang et al. https://doi.org/10.1016/j.ejrh.2025.102345
- 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 https://doi.org/10.1590/1809-4430-eng.agric.v42n4e20220035/2022
- A climate-conditioned catastrophe risk model for UK flooding P. Bates et al. https://doi.org/10.5194/nhess-23-891-2023
- Regional-scale flood mapping using naive Bayes and remote sensing: towards local relevance V. Kuchinski & R. Cauduro Dias de Paiva https://doi.org/10.1080/02626667.2025.2506749
- A review of the literature on spatial prediction of floods using machine learning models S. Hajji et al. https://doi.org/10.1007/s11852-025-01158-2
- Recommendations to improve the interpretation of global flood forecasts to support international humanitarian operations for tropical cyclones L. Speight et al. https://doi.org/10.1111/jfr3.12952
- Alternate pathway for regional flood frequency analysis in data-sparse region N. Mangukiya & A. Sharma https://doi.org/10.1016/j.jhydrol.2024.130635
- Comparative assessment of metaheuristic-optimized SVR models for river discharge prediction in the Dez watershed H. Babaali & R. Dehghani https://doi.org/10.1016/j.dwt.2025.101531
- Evaluating the effects of preprocessing, method selection, and hyperparameter tuning on SAR-based flood mapping and water depth estimation J. Travert et al. https://doi.org/10.5194/nhess-26-2387-2026
- An update to a regional frequency analysis to global extreme sea levels T. Collings et al. https://doi.org/10.1016/j.coastaleng.2026.105007
- Monitoring surface water in floodplains by satellites: Progress, challenges, and perspectives Y. Lin & C. Song https://doi.org/10.1016/j.jhydrol.2025.134458
- Evaluating the impact of gridded population datasets variability on flood exposure estimates across South Asia J. Zhang et al. https://doi.org/10.1080/19475705.2025.2481996
- River channel change can affect flood hazard and impact substantially L. Hawker et al. https://doi.org/10.1038/s43247-026-03517-9
- Mapping agricultural fragility in India through a yield gap vulnerability framework: a national-scale machine learning assessment R. Ranjan et al. https://doi.org/10.1016/j.jenvman.2026.130802
- Deep learning methods for flood mapping: a review of existing applications and future research directions R. Bentivoglio et al. https://doi.org/10.5194/hess-26-4345-2022
- Performance benchmarking on several regression models applied in urban flash flood risk assessment H. Hu et al. https://doi.org/10.1007/s11069-023-06341-y
- Continental Scale Regional Flood Frequency Analysis: Combining Enhanced Datasets and a Bayesian Framework D. Alexandre et al. https://doi.org/10.3390/hydrology11080119
- Calibrated uncertainty for continental design flood estimation using conformal probabilistic machine learning M. El baida et al. https://doi.org/10.1016/j.jhydrol.2026.135864
- Simulation of monthly river flow using SVR neural network improved with population-based optimization algorithms A. Kohansarbaz et al. https://doi.org/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 https://doi.org/10.1029/2023WR034947
- The representation of rivers in operational ocean forecasting systems: a review P. Matte et al. https://doi.org/10.5194/sp-5-opsr-19-2025
- Prediction of flood quantiles at ungauged catchments for the contiguous USA using Artificial Neural Networks V. Filipova et al. https://doi.org/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. https://doi.org/10.1007/s44288-024-00084-4
- Impacts of spatial-temporal rainfall structures and antecedent wetness on flood variability at the catchment scale W. Yang et al. https://doi.org/10.1016/j.jhydrol.2026.135019
- A dataset of gridded precipitation intensity-duration-frequency curves in Qinghai-Tibet Plateau Z. Ren et al. https://doi.org/10.1038/s41597-024-04362-1
- African Q99 prediction model: Hydrological clustering and regression machine learning for extreme flow prediction in African catchments M. El baida et al. https://doi.org/10.1016/j.jafrearsci.2026.106225
Saved (final revised paper)
Latest update: 30 Oct 2026
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...
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