Articles | Volume 29, issue 3
https://doi.org/10.5194/npg-29-301-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-29-301-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Aug 2022
Research article |
| 01 Aug 2022
| 01 Aug 2022
Integrated hydrodynamic and machine learning models for compound flooding prediction in a data-scarce estuarine delta
Joko Sampurno
CORRESPONDING AUTHOR
Earth and Life Institute (ELI), Université Catholique de Louvain (UCLouvain), Louvain-la-Neuve, 1348, Belgium
Department of Physics, Fakultas MIPA, Universitas Tanjungpura, Pontianak, 78124, Indonesia
Valentin Vallaeys
Earth and Life Institute (ELI), Université Catholique de Louvain (UCLouvain), Louvain-la-Neuve, 1348, Belgium
Randy Ardianto
Pontianak Maritime Meteorological Station, Pontianak, 78111, Indonesia
Emmanuel Hanert
Earth and Life Institute (ELI), Université Catholique de Louvain (UCLouvain), Louvain-la-Neuve, 1348, Belgium
Institute of Mechanics, Materials and Civil Engineering (iMMC), Université Catholique de Louvain (UCLouvain), Louvain-la-Neuve, 1348, Belgium
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Cited
29 citations as recorded by crossref.
- Anticipated Compound Flooding in Miami-Dade Under Extreme Hydrometeorological Events A. Gumbs et al. https://doi.org/10.3390/hydrology13010034
- AI-Assisted Urban Flood Prevention Services Decision-Making Framework With Multi-Dimensional Data Fusion via Govern-Intranet X. Fan et al. https://doi.org/10.4018/IJWSR.375425
- Research progresses and prospects of multi-sphere compound extremes from the Earth System perspective Z. Hao & Y. Chen https://doi.org/10.1007/s11430-023-1201-y
- 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
- Classification and detection of natural disasters using machine learning and deep learning techniques: A review K. Abraham et al. https://doi.org/10.1007/s12145-023-01205-2
- SPATIALLY EXPLICIT MACHINE LEARNING MODEL FOR FLOOD INUNDATION AUGMENTED WITH HYDRODYNAMIC MODELING K. LIU et al. https://doi.org/10.2208/journalofjsce.24-16188
- A Review of Application of Machine Learning in Storm Surge Problems Y. Qin et al. https://doi.org/10.3390/jmse11091729
- Multivariate multi-step LSTM model for flood runoff prediction: a case study on the Godavari River Basin in India N. Garg et al. https://doi.org/10.2166/wcc.2023.374
- Analyzing the Resilience of Active Distribution Networks to Hazardous Weather Considering Cyber–Physical Interdependencies Z. Li et al. https://doi.org/10.1016/j.eng.2024.10.004
- Influence of hydrograph characteristics and littoral sandbars on flooding dynamics in a small-scale estuary K. Soto-Rivas & R. Flores https://doi.org/10.1016/j.ejrh.2026.103171
- A machine learning approach to evaluate coastal risks related to extreme weather events in the Veneto region (Italy) M. Dal Barco et al. https://doi.org/10.1016/j.ijdrr.2024.104526
- Impacts of rainstorm characteristics on flood inundation mitigation performance of LID measures throughout an urban catchment Z. Zhou et al. https://doi.org/10.1016/j.jhydrol.2023.129841
- PyFlood: Rapid high-resolution coastal flood mapping with digital elevation model, land cover and water level data A. Santos Cruz et al. https://doi.org/10.1016/j.envsoft.2026.107010
- A Comprehensive Review of Machine Learning for Water Quality Prediction over the Past Five Years X. Yan et al. https://doi.org/10.3390/jmse12010159
- The Impact of Climate Change and Urbanization on Compound Flood Risks in Coastal Areas: A Comprehensive Review of Methods X. Ruan et al. https://doi.org/10.3390/app142110019
- Developing a combined numerical-machine learning model for distributed flood routing H. Saghi et al. https://doi.org/10.1007/s40899-024-01180-6
- Forecasting of compound ocean-fluvial floods using machine learning S. Moradian et al. https://doi.org/10.1016/j.jenvman.2024.121295
- Review article: A comprehensive review of compound flooding literature with a focus on coastal and estuarine regions J. Green et al. https://doi.org/10.5194/nhess-25-747-2025
- Physics-aware machine learning revolutionizes scientific paradigm for process-based modeling in hydrology Q. Xu et al. https://doi.org/10.1016/j.earscirev.2025.105276
- Flood Hazard and Management in Cambodia: A Review of Activities, Knowledge Gaps, and Research Direction S. Phy et al. https://doi.org/10.3390/cli10110162
- A cluster-based temporal attention approach for predicting cyclone-induced compound flood dynamics S. Daramola et al. https://doi.org/10.1016/j.envsoft.2025.106499
- Application of Idealised Modelling and Data Analysis for Assessing the Compounding Effects of Sea Level Rise and Altered Riverine Inflows on Estuarine Tidal Dynamics D. Khojasteh et al. https://doi.org/10.3390/jmse11040815
- Compound flooding risks in coastal cities: a review of causes, mechanisms, and assessment methodologies E. Jahani et al. https://doi.org/10.1007/s11069-026-08183-w
- Artificial intelligence for climate prediction of extremes: State of the art, challenges, and future perspectives S. Materia et al. https://doi.org/10.1002/wcc.914
- Estimating water levels and discharges in tidal rivers and estuaries: Review of machine learning approaches A. Mihel et al. https://doi.org/10.1016/j.envsoft.2024.106033
- Application of a coupled mechanistic and data-driven model for water level prediction considering the temporal and spatial effects of runoff evolution in cascade hydropower stations Y. Zhang et al. https://doi.org/10.1016/j.jhydrol.2026.135057
- Mapping Compound Flooding Risks for Urban Resilience in Coastal Zones: A Comprehensive Methodological Review H. Sun et al. https://doi.org/10.3390/rs16020350
- Compound flooding: A review on assessment framework A. Akter & S. Ataullah https://doi.org/10.1007/s11852-025-01153-7
- Evolution of Flood Prediction and Forecasting Models for Flood Early Warning Systems: A Scoping Review N. Byaruhanga et al. https://doi.org/10.3390/w16131763
29 citations as recorded by crossref.
- Anticipated Compound Flooding in Miami-Dade Under Extreme Hydrometeorological Events A. Gumbs et al. https://doi.org/10.3390/hydrology13010034
- AI-Assisted Urban Flood Prevention Services Decision-Making Framework With Multi-Dimensional Data Fusion via Govern-Intranet X. Fan et al. https://doi.org/10.4018/IJWSR.375425
- Research progresses and prospects of multi-sphere compound extremes from the Earth System perspective Z. Hao & Y. Chen https://doi.org/10.1007/s11430-023-1201-y
- 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
- Classification and detection of natural disasters using machine learning and deep learning techniques: A review K. Abraham et al. https://doi.org/10.1007/s12145-023-01205-2
- SPATIALLY EXPLICIT MACHINE LEARNING MODEL FOR FLOOD INUNDATION AUGMENTED WITH HYDRODYNAMIC MODELING K. LIU et al. https://doi.org/10.2208/journalofjsce.24-16188
- A Review of Application of Machine Learning in Storm Surge Problems Y. Qin et al. https://doi.org/10.3390/jmse11091729
- Multivariate multi-step LSTM model for flood runoff prediction: a case study on the Godavari River Basin in India N. Garg et al. https://doi.org/10.2166/wcc.2023.374
- Analyzing the Resilience of Active Distribution Networks to Hazardous Weather Considering Cyber–Physical Interdependencies Z. Li et al. https://doi.org/10.1016/j.eng.2024.10.004
- Influence of hydrograph characteristics and littoral sandbars on flooding dynamics in a small-scale estuary K. Soto-Rivas & R. Flores https://doi.org/10.1016/j.ejrh.2026.103171
- A machine learning approach to evaluate coastal risks related to extreme weather events in the Veneto region (Italy) M. Dal Barco et al. https://doi.org/10.1016/j.ijdrr.2024.104526
- Impacts of rainstorm characteristics on flood inundation mitigation performance of LID measures throughout an urban catchment Z. Zhou et al. https://doi.org/10.1016/j.jhydrol.2023.129841
- PyFlood: Rapid high-resolution coastal flood mapping with digital elevation model, land cover and water level data A. Santos Cruz et al. https://doi.org/10.1016/j.envsoft.2026.107010
- A Comprehensive Review of Machine Learning for Water Quality Prediction over the Past Five Years X. Yan et al. https://doi.org/10.3390/jmse12010159
- The Impact of Climate Change and Urbanization on Compound Flood Risks in Coastal Areas: A Comprehensive Review of Methods X. Ruan et al. https://doi.org/10.3390/app142110019
- Developing a combined numerical-machine learning model for distributed flood routing H. Saghi et al. https://doi.org/10.1007/s40899-024-01180-6
- Forecasting of compound ocean-fluvial floods using machine learning S. Moradian et al. https://doi.org/10.1016/j.jenvman.2024.121295
- Review article: A comprehensive review of compound flooding literature with a focus on coastal and estuarine regions J. Green et al. https://doi.org/10.5194/nhess-25-747-2025
- Physics-aware machine learning revolutionizes scientific paradigm for process-based modeling in hydrology Q. Xu et al. https://doi.org/10.1016/j.earscirev.2025.105276
- Flood Hazard and Management in Cambodia: A Review of Activities, Knowledge Gaps, and Research Direction S. Phy et al. https://doi.org/10.3390/cli10110162
- A cluster-based temporal attention approach for predicting cyclone-induced compound flood dynamics S. Daramola et al. https://doi.org/10.1016/j.envsoft.2025.106499
- Application of Idealised Modelling and Data Analysis for Assessing the Compounding Effects of Sea Level Rise and Altered Riverine Inflows on Estuarine Tidal Dynamics D. Khojasteh et al. https://doi.org/10.3390/jmse11040815
- Compound flooding risks in coastal cities: a review of causes, mechanisms, and assessment methodologies E. Jahani et al. https://doi.org/10.1007/s11069-026-08183-w
- Artificial intelligence for climate prediction of extremes: State of the art, challenges, and future perspectives S. Materia et al. https://doi.org/10.1002/wcc.914
- Estimating water levels and discharges in tidal rivers and estuaries: Review of machine learning approaches A. Mihel et al. https://doi.org/10.1016/j.envsoft.2024.106033
- Application of a coupled mechanistic and data-driven model for water level prediction considering the temporal and spatial effects of runoff evolution in cascade hydropower stations Y. Zhang et al. https://doi.org/10.1016/j.jhydrol.2026.135057
- Mapping Compound Flooding Risks for Urban Resilience in Coastal Zones: A Comprehensive Methodological Review H. Sun et al. https://doi.org/10.3390/rs16020350
- Compound flooding: A review on assessment framework A. Akter & S. Ataullah https://doi.org/10.1007/s11852-025-01153-7
- Evolution of Flood Prediction and Forecasting Models for Flood Early Warning Systems: A Scoping Review N. Byaruhanga et al. https://doi.org/10.3390/w16131763
Saved (final revised paper)
Latest update: 07 Jun 2026
Short summary
In this study, we successfully built and evaluated machine learning models for predicting water level dynamics as a proxy for compound flooding hazards in a data-scarce delta. The issues that we tackled here are data scarcity and low computational resources for building flood forecasting models. The proposed approach is suitable for use by local water management agencies in developing countries that encounter these issues.
In this study, we successfully built and evaluated machine learning models for predicting water...
