Articles | Volume 26, issue 16
https://doi.org/10.5194/hess-26-4345-2022
© 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/hess-26-4345-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Review article
| 
25 Aug 2022
Review article |
| 25 Aug 2022
| 25 Aug 2022
Deep learning methods for flood mapping: a review of existing applications and future research directions
Roberto Bentivoglio
CORRESPONDING AUTHOR
Department of Water Management, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, the Netherlands
Elvin Isufi
Department of Intelligent Systems, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, the Netherlands
Sebastian Nicolaas Jonkman
Department of Hydraulic Engineering, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, the Netherlands
Riccardo Taormina
Department of Water Management, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, the Netherlands
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- A Novel Estimation of the Composite Hazard of Landslides and Flash Floods Utilizing an Artificial Intelligence Approach M. Wahba et al. 10.3390/w15234138
- Rainfall-driven machine learning models for accurate flood inundation mapping in Karachi, Pakistan U. Rasool et al. 10.1016/j.uclim.2023.101573
- A Review of Hydrodynamic and Machine Learning Approaches for Flood Inundation Modeling F. Karim et al. 10.3390/w15030566
- FFM: Flood Forecasting Model Using Federated Learning M. Farooq et al. 10.1109/ACCESS.2023.3252896
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- MatFlood: An efficient algorithm for mapping flood extent and depth A. Enriquez et al. 10.1016/j.envsoft.2023.105829
- Fast high-fidelity flood inundation map generation by super-resolution techniques Z. Yin et al. 10.2166/hydro.2024.228
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- Artificial neural network-based multi-input multi-output model for short-term storm surge prediction on the southeast coast of China Y. Qin et al. 10.1016/j.oceaneng.2024.116915
- Comprehensive Flood Risk Assessment: State of the Practice N. Grigg 10.3390/hydrology10020046
- NeuralFlood: an AI-driven flood susceptibility index J. Lin et al. 10.3389/frwa.2023.1291305
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- An efficient 2-D flood inundation modelling based on a data-driven approach S. Chiang et al. 10.1016/j.ejrh.2024.101741
- Rapid spatio-temporal flood modelling via hydraulics-based graph neural networks R. Bentivoglio et al. 10.5194/hess-27-4227-2023
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- S1S2-Water: A Global Dataset for Semantic Segmentation of Water Bodies From Sentinel- 1 and Sentinel-2 Satellite Images M. Wieland et al. 10.1109/JSTARS.2023.3333969
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- Leveraging Transfer Learning in LSTM Neural Networks for Data-Efficient Burst Detection in Water Distribution Systems K. Glynis et al. 10.1007/s11269-023-03637-3
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- Flood Detection with SAR: A Review of Techniques and Datasets D. Amitrano et al. 10.3390/rs16040656
- Flood Inundation and Depth Mapping Using Unmanned Aerial Vehicles Combined with High-Resolution Multispectral Imagery K. Wienhold et al. 10.3390/hydrology10080158
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- Modeling rainfall-induced 2D inundation simulation based on the ANN-derived models with precipitation and water-level measurements at roadside IoT sensors S. Wu 10.1038/s41598-023-44276-3
- Connecting hydrological modelling and forecasting from global to local scales: Perspectives from an international joint virtual workshop A. Dasgupta et al. 10.1111/jfr3.12880
- An Efficient U-Net Model for Improved Landslide Detection from Satellite Images N. Chandra et al. 10.1007/s41064-023-00232-4
Latest update: 24 Apr 2024
Short summary
Deep learning methods have been increasingly used in flood management to improve traditional techniques. While promising results have been obtained, our review shows significant challenges in building deep learning models that can (i) generalize across multiple scenarios, (ii) account for complex interactions, and (iii) perform probabilistic predictions. We argue that these shortcomings could be addressed by transferring recent fundamental advancements in deep learning to flood mapping.
Deep learning methods have been increasingly used in flood management to improve traditional...
