Journal article

A rapid flood inundation modelling framework using deep learning with spatial reduction and reconstruction

Yuerong Zhou, Wenyan Wu, Rory Nathan, Quan J Wang



Traditional approaches to inundation modelling are computationally intensive and thus not well suited to assessing the uncertainty involved in estimating flood inundation surfaces for planning, design and forecasting purposes. In this study, a rapid flood inundation modelling framework is developed, consisting of a novel spatial reduction and reconstruction (SRR) approach and a deep learning (DL) modelling component. The SRR approach is developed to reduce computational cost by identifying representative locations of inundation surfaces where water levels are simulated using DL models, and to efficiently reconstruct inundation surfaces based on simulated water level information. The DL model..

View full abstract


Awarded by LIEF Grant

Funding Acknowledgements

We thank Hydrology and Risk Consulting (HARC) for providing the TUFLOW 2D hydrodynamic model configuration and SunWater for their permission to use the Burnett River as a case study. We also thank BMT for providing the TUFLOW license to conduct the TUFLOW simulations. This research was undertaken using the LIEF HPC-GPGPU Facility hosted at The University of Melbourne. This Facility was established with the assistance of LIEF Grant [grant number LE170100200]. At last, for a range of open source software and libraries used in this study, including but not limited to Python programming language (version 3.7), PyTorch, Numpy, Scipy, Fiona, QGIS, NetCDF4, Geopandas, and Geospatial Data Abstraction Library (GDAL), we would like to thank the developers and the open source community for all contributions.