Articles | Volume 23, issue 2
https://doi.org/10.5194/nhess-23-809-2023
© Author(s) 2023. 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-23-809-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 Feb 2023
Research article |
| 24 Feb 2023
| 24 Feb 2023
Transferability of data-driven models to predict urban pluvial flood water depth in Berlin, Germany
Department of Hydrology and Climatology, Institute of Environmental Science and Geography, University of Potsdam, Potsdam, Germany
Georgy Ayzel
Department of Hydrology and Climatology, Institute of Environmental Science and Geography, University of Potsdam, Potsdam, Germany
Axel Bronstert
Department of Hydrology and Climatology, Institute of Environmental Science and Geography, University of Potsdam, Potsdam, Germany
Maik Heistermann
Department of Hydrology and Climatology, Institute of Environmental Science and Geography, University of Potsdam, Potsdam, Germany
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- An explainable and transferable deep learning framework for spatiotemporal urban flood prediction by integrating Vision Transformer and U-Net J. Qiu et al. https://doi.org/10.1016/j.watres.2026.125504
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- Advancing rapid urban flood prediction: a spatiotemporal deep learning approach with uneven rainfall and attention mechanism Y. Shao et al. https://doi.org/10.2166/hydro.2024.024
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- Data driven real-time prediction of urban floods with spatial and temporal distribution S. Berkhahn & I. Neuweiler https://doi.org/10.1016/j.hydroa.2023.100167
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- An efficient 2-D flood inundation modelling based on a data-driven approach S. Chiang et al. https://doi.org/10.1016/j.ejrh.2024.101741
- Brief communication: What do we need to know? Ten questions about climate and water challenges in Berlin-Brandenburg P. Alencar et al. https://doi.org/10.5194/nhess-25-4043-2025
- Generalist–specialist transfer learning for cross-city spatial generalization in pluvial flood prediction Z. Li et al. https://doi.org/10.1016/j.watres.2026.126650
37 citations as recorded by crossref.
- Unlocking full-domain flood inundation prediction from sparse point supervision in deep-learning models J. Xie et al. https://doi.org/10.1016/j.watres.2026.126760
- Urban Waterlogging Prediction Based on Time Lag Correlation Analysis and Multi-Model Coupling X. Lei et al. https://doi.org/10.1007/s11269-026-04635-x
- An upgraded GIS-based multi-criteria decision-making approach for flood control prioritization mapping: Case study of West Dallas-Fort Worth metroplex Y. Zhang & F. Jaber https://doi.org/10.1016/j.ecoinf.2025.103573
- Accelerating urban flood prediction using a dual-stream Transformer‑CNN model with spatiotemporal feature fusion and uncertainty quantification W. Gao et al. https://doi.org/10.1016/j.jhydrol.2026.135119
- Geospatial and Deep Learning Approaches for Modeling Floodwater Depth in Urbanized Areas J. Blay & L. Hashemi-Beni https://doi.org/10.3390/rs18010060
- Feasibility-guided evolutionary optimization of pump station design and operation in water networks T. Faúndez-Lizama et al. https://doi.org/10.1038/s41598-025-17630-w
- An explainable and transferable deep learning framework for spatiotemporal urban flood prediction by integrating Vision Transformer and U-Net J. Qiu et al. https://doi.org/10.1016/j.watres.2026.125504
- Enhancing cross-regional transferability of super-resolution-based flood surrogate models for data-scarce catchments W. Song & M. Guan https://doi.org/10.1016/j.watres.2026.125799
- A highly generalizable data-driven model for spatiotemporal urban flood dynamics real-time forecasting based on coupled CNN and ConvLSTM W. Lou et al. https://doi.org/10.5194/hess-30-1625-2026
- Generalized Methodology for Two-Dimensional Flood Depth Prediction Using ML-Based Models M. Soliman et al. https://doi.org/10.3390/hydrology12090223
- An attention-based super-resolution model for high-accuracy urban pluvial flood forecasting in coastal megacities H. Shen et al. https://doi.org/10.1016/j.jhydrol.2026.135330
- A physically informed domain-independent data-driven inundation forecast model F. Schmid et al. https://doi.org/10.1016/j.watres.2025.124819
- An Open-Source Urban Digital Twin for Enhancing Outdoor Thermal Comfort in the City of Huelva (Spain) V. Lopez-Cabeza et al. https://doi.org/10.3390/smartcities8050160
- Comparing strategies for training LSTM models for street-scale urban flood prediction in Norfolk, Virginia J. Jeong et al. https://doi.org/10.1016/j.ejrh.2026.103405
- A Spatiotemporal Deep Learning Approach for Urban Pluvial Flood Forecasting with Multi-Source Data B. Burrichter et al. https://doi.org/10.3390/w15091760
- A Conceptual Approach to Predicting Seismic Events and Flood Risks Using Convolutional Neural Networks M. Rehan et al. https://doi.org/10.33093/jiwe.2025.4.2.9
- Advancing rapid urban flood prediction: a spatiotemporal deep learning approach with uneven rainfall and attention mechanism Y. Shao et al. https://doi.org/10.2166/hydro.2024.024
- Physics-Guided Deep Learning for Spatiotemporal Evolution of Urban Pluvial Flooding H. Woo et al. https://doi.org/10.3390/w17081239
- Preface: Advances in pluvial and fluvial flood forecasting and assessment and flood risk management C. Prieto et al. https://doi.org/10.5194/nhess-24-3381-2024
- A hybrid statistical–dynamical framework for compound coastal flooding analysis Z. Wang et al. https://doi.org/10.1088/1748-9326/ad96ce
- Improving urban flood prediction using LSTM-DeepLabv3+ and Bayesian optimization with spatiotemporal feature fusion Z. Situ et al. https://doi.org/10.1016/j.jhydrol.2024.130743
- 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
- When Land Use/Land Cover Misleads: Limitations in Data-Driven Flood and Landslide Susceptibility Assessment S. Fardin et al. https://doi.org/10.3390/geohazards7030094
- Data driven real-time prediction of urban floods with spatial and temporal distribution S. Berkhahn & I. Neuweiler https://doi.org/10.1016/j.hydroa.2023.100167
- Enhancing generalizability of data-driven urban flood models by incorporating contextual information T. Cache et al. https://doi.org/10.5194/hess-28-5443-2024
- Comprehensive Overview of Flood Modeling Approaches: A Review of Recent Advances V. Kumar et al. https://doi.org/10.3390/hydrology10070141
- Flood Water Depth Prediction with Convolutional Temporal Attention Networks P. Chaudhary et al. https://doi.org/10.3390/w16091286
- 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
- A Study on Flood Susceptibility Mapping in the Poyang Lake Basin Based on Machine Learning Model Comparison and SHapley Additive exPlanations Interpretation Z. Li et al. https://doi.org/10.3390/w17202955
- Pluvial flood susceptibility mapping for data-scarce urban areas using graph attention network and basic flood conditioning factors Z. Wang et al. https://doi.org/10.1080/10106049.2023.2275692
- Real-time probabilistic spatiotemporal forecasting of building functionality in flood emergencies: a deep learning approach to facilitate community evacuation planning L. Xie et al. https://doi.org/10.1080/15732479.2025.2529469
- Machine Learning for Flood Resiliency—Current Status and Unexplored Directions V. Uddameri & E. Hernandez https://doi.org/10.3390/environments12080259
- Investigation of pluvial flash flood loads on overpasses for the city of Montreal O. Ziya et al. https://doi.org/10.1007/s44327-025-00172-1
- Coupled sink and flow accumulation analyses with single flow direction and multiple flow direction algorithms N. Bowsher et al. https://doi.org/10.2166/hydro.2023.123
- An efficient 2-D flood inundation modelling based on a data-driven approach S. Chiang et al. https://doi.org/10.1016/j.ejrh.2024.101741
- Brief communication: What do we need to know? Ten questions about climate and water challenges in Berlin-Brandenburg P. Alencar et al. https://doi.org/10.5194/nhess-25-4043-2025
- Generalist–specialist transfer learning for cross-city spatial generalization in pluvial flood prediction Z. Li et al. https://doi.org/10.1016/j.watres.2026.126650
Saved (final revised paper)
Latest update: 15 Sep 2026
Short summary
Data-driven models are becoming more of a surrogate that overcomes the limitations of the computationally expensive 2D hydrodynamic models to map urban flood hazards. However, the model's ability to generalize outside the training domain is still a major challenge. We evaluate the performance of random forest and convolutional neural networks to predict urban floodwater depth and investigate their transferability outside the training domain.
Data-driven models are becoming more of a surrogate that overcomes the limitations of the...
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