Published August 5, 2024
| Version v2.0.0
Software
Open
FloodSformer: python code v2.0.0
Authors/Creators
- 1. University of Parma
-
2.
Nvidia (United States)
Description
This is the official implementation of the FloodSformer model v2.0.0. See our papers:
- Pianforini et al. (2024). Real-Time Flood Maps Forecasting for Dam-Break Scenarios with a Transformer-Based Deep Learning Model. Journal of Hydrology, 635, 131169. https://doi.org/10.1016/j.jhydrol.2024.131169
- Pianforini et al. (2025). FloodSformer: A transformer-based data-driven model for predicting the 2-D dynamics of fluvial floods. Environmental Modelling & Software. https://doi.org/10.1016/j.envsoft.2025.106599
In this repository, we provide the PyTorch code for training and testing out proposed FloodSformer model.
Detailed information on the repository content are provided in the README.md file.
Acknowledgements
This research was granted by University of Parma through the action “Bando di Ateneo 2022 per la ricerca” and “Bando di Ateneo 2024 per la ricerca”. RV and SD acknowledge financial support from the PNRR MUR project ECS_00000033_ECOSISTER. This research also benefits from the HPC facility of the University of Parma. Finally, the Authors acknowledge the CINECA award under the ISCRA initiative, for the availability of high-performance computing resources and support (projects AMNERIS and MOZART).
Files
mpianforini/FloodSformer-v2.0.0.zip
Files
(460.9 kB)
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Additional details
Related works
- Is supplement to
- Software: https://github.com/mpianforini/FloodSformer/tree/v2.0.0 (URL)
- Dataset: 10.5281/zenodo.10878384 (DOI)
- Dataset: 10.5281/zenodo.11471387 (DOI)
Software
- Programming language
- Python
References
- Pianforini, M., Dazzi, S., Pilzer, A., & Vacondio, R. (2024). Real-time flood maps forecasting for dam-break scenarios with a transformer-based deep learning model. Journal of Hydrology, 635, 131169. https://doi.org/10.1016/J.JHYDROL.2024.131169
- Pianforini, M., Dazzi, S., Pilzer, A., & Vacondio, R. (2025). FloodSformer: A transformer-based data-driven model for predicting the 2-D dynamics of fluvial floods. Environmental Modelling & Software. https://doi.org/10.1016/j.envsoft.2025.106599