A weighted cellular automata 2D inundation model for rapid flood analysis
Environmental Modelling and Software
Open Access funded by Economic and Social Research Council. Available under a Creative Commons licence: https://creativecommons.org/licenses/by/4.0/
To achieve fast flood modelling for large scale problems, a two-dimensional cellular automata based model has been developed in this study. This model uses simple transition rules and a weight-based system instead of solving complex shallow water equations. The cellular automata feature allows the proposed model to be implemented in a parallel computing environment such that the modelling efficiency is improved significantly due to the combination of simplification and parallelisation. The proposed model has been tested on four hypothetical case studies and one real world example, and the outputs compared to those from traditional physically based hydraulic models. Results show that the proposed model is capable of producing good agreement with other hydraulic models, using a fraction of computational time. In the case of the real world example, the model run times are up to 8 times faster than the a widely use commercial hydraulic model. This rapid and accurate attributes of the proposed model have demonstrated its applicability for quick flood analysis in large modelling systems
The authors would like to acknowledge the funding provided by the UK Engineering and Physical Sciences Research Council, grant EP/H015736/1 (Simplified Dual-Drainage Modelling for Flood Risk Assessment in Urban Areas). The authors would also like to thank the UK Environment Agency for the EA benchmarks datasets and the Torquay Council for the LIDAR datasets. Furthermore, the authors would like to thank Mike Gibson for the help given during the development of the OpenMP implementation of the CADDIES CA API and for the helpful comments about this document. Finally, the authors would like to acknowledge the support of NVIDIA Corporation with the donation of the Tesla K20c GPU used in this research and the support of Innovyze for the license of the InfoWorks ICM 3.0 software.
This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.
Vol. 84, pp. 378–394