Zhou Yuerong, Wu Wenyan, Nathan Rory, Wang Quan J
Department of Infrastructure Engineering, Faculty of Engineering and Information Technology, The University of Melbourne, Victoria, Australia.
MethodsX. 2021 Sep 24;8:101527. doi: 10.1016/j.mex.2021.101527. eCollection 2021.
Fast and accurate modelling of flood inundation has gained increasing attention in recent years. One approach gaining popularity recently is the development of emulation models using data driven methods, such as artificial neural networks. These emulation models are often developed to model flood depth for each grid cell in the modelling domain in order to maintain accurate spatial representation of the flood inundation surface. This leads to redundancy in modelling, as well as difficulties in achieving good model performance across floodplains where there are limited data available. In this paper, a spatial reduction and reconstruction (SRR) method is developed to (1) identify representative locations within the model domain where water levels can be used to represent flood inundation surface using deep learning models; and (2) reconstruct the flood inundation surface based on water levels simulated at these representative locations. The SRR method is part of the SRR-Deep-Learning framework for flood inundation modelling and therefore, it needs to be used together with data driven models. The SRR method is programmed using the Python programming language and is freely available from https://github.com/yuerongz/SRR-method.•The SRR method identifies locations which are representative of flood inundation behavior in surrounding areas.•The representative locations selected following the SRR method have sufficient flood data for developing emulation models.•Flood inundation surfaces can be reconstructed using the SRR method with a detection rate of above 99%.
近年来，快速准确的洪水淹没建模越来越受到关注。最近一种越来越流行的方法是使用数据驱动方法（如人工神经网络）开发仿真模型。这些仿真模型通常用于对建模域中每个网格单元的洪水深度进行建模，以保持洪水淹没表面的准确空间表示。这导致建模过程中出现冗余，并且在数据有限的洪泛区难以实现良好的模型性能。本文提出了一种空间缩减与重建（SRR）方法，用于：（1）在模型域内识别代表性位置，利用深度学习模型通过水位来表示洪水淹没表面；（2）基于在这些代表性位置模拟的水位重建洪水淹没表面。SRR方法是洪水淹没建模的SRR深度学习框架的一部分，因此，它需要与数据驱动模型一起使用。SRR方法使用Python编程语言进行编程，可从https://github.com/yuerongz/SRR-method免费获取。
•SRR方法可识别代表周边地区洪水淹没行为的位置。
•按照SRR方法选择的代表性位置有足够的洪水数据用于开发仿真模型。
•使用SRR方法重建洪水淹没表面的检测率可超过99%。
