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A rapid flood inundation modelling framework using deep learning with spatial reduction and reconstruction
Environmental Modelling & Software ( IF 4.9 ) Pub Date : 2021-06-11 , DOI: 10.1016/j.envsoft.2021.105112
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 includes a built-in input selection layer to simplify the model development process, and a Long Short-Term Memory layer for time series modelling. The accuracy and efficiency of the SRR-DL framework is assessed by application to a real-world river system where the inundation of over 3 million grid cells can be simulated in 4 s.



中文翻译:

使用深度学习与空间缩减和重建的快速洪水淹没建模框架

传统的淹没建模方法是计算密集型的,因此不太适合评估为规划、设计和预测目的估计洪水淹没面所涉及的不确定性。在这项研究中,开发了一个快速洪水淹没建模框架,包括一种新颖的空间缩减和重建 (SRR) 方法和一个深度学习 (DL) 建模组件。开发 SRR 方法是为了通过识别使用 DL 模型模拟水位的淹没面的代表性位置来降低计算成本,并根据模拟的水位信息有效地重建淹没面。DL 模型包括一个用于简化模型开发过程的内置输入选择层,以及一个用于时间序列建模的 Long Short-Term Memory 层。

更新日期:2021-06-24
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