An efficient inundation model is necessary for emergency flood responses during storm events. Cellular automata (CA)-based flood models have been proven to produce rapid results while maintaining a certain degree of accuracy. However, the need for computational resources dramatically increases when the number of grid cells increases. Digital elevation model (DEM)-based models generate results even faster, but the simplified governing equations within the models fail to reflect temporal flood evolution. To achieve rapid flood modeling while maintaining model simplicity, a novel two-dimensional hybrid inundation model (HIM) was developed by combining the CA- and DEM-based concepts. Given the temporal flood evolution generated by the CA concept, final finer-scale predictions were obtained by applying the DEM-based concept. The performance of this model was compared to those of widely used, physically based hydraulic models using three UK Environment Agency (EA) benchmark test cases. The HIM yielded consistent prediction results but was faster than the CA-based model. Finally, a comparison was made against flood observations, and the overall root mean squared error (RMSE) for flood depth was 0.388–0.400 m. Considering the uncertainty in the observed flood depths, the HIM shows promising potential to serve as an intermediate tool for emergency response in practical cases.

中文翻译：

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基于元胞自动机和数字高程模型的混合洪水快速评估方法

有效的淹没模型对于暴风雨期间的紧急洪水响应是必不可少的。事实证明，基于元胞自动机（CA）的洪水模型可产生快速结果，同时保持一定程度的准确性。然而，当网格单元的数量增加时，对计算资源的需求急剧增加。基于数字高程模型（DEM）的模型生成结果的速度更快，但是模型中的简化控制方程无法反映时间洪水的演变。为了在保持模型简单性的同时实现快速洪水建模，通过结合基于CA和DEM的概念，开发了一种新型的二维混合淹没模型（HIM）。给定由CA概念产生的时间洪水演变，通过应用基于DEM的概念可以获得最终的更精细规模的预测。使用三个英国环境局（EA）基准测试用例，将该模型的性能与广泛使用的基于物理的液压模型的性能进行了比较。HIM产生一致的预测结果，但比基于CA的模型要快。最后，与洪水观测结果进行了比较，洪水深度的整体均方根误差（RMSE）为0.388–0.400 m。考虑到所观察到的洪水深度的不确定性，HIM显示出有希望的潜力，可在实际情况下用作应急响应的中间工具。洪水深度的整体均方根误差（RMSE）为0.388–0.400 m。考虑到所观察到的洪水深度的不确定性，HIM显示出有希望的潜力，可在实际情况下用作应急响应的中间工具。洪水深度的整体均方根误差（RMSE）为0.388–0.400 m。考虑到所观察到的洪水深度的不确定性，HIM显示出有希望的潜力，可在实际情况下用作应急响应的中间工具。