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A deep learning model for predicting river flood depth and extent
Environmental Modelling & Software ( IF 4.9 ) Pub Date : 2021-08-31 , DOI: 10.1016/j.envsoft.2021.105186
Hossein Hosseiny 1
Affiliation  

This paper presents an innovative deep learning (DL) framework to (a) automatically identify river geometry and flood extent, and (b) predict river flooding depth. To do that, U-Net, an advanced convolutional neural network (CNN), was modified and given the designation of U-NetRiver. With the modification, the model received an input composite image with two bands of ground elevation and flooding discharge, and the output was water depth. The model was trained and validated based on the outputs from iRIC (a two-dimensional hydraulic model) for a segment of the Green River in the state of Utah. The results showed that the U-NetRiver could identify the river shape and wetted areas for flooded regions automatically. The maximum difference of predicted river depth obtained from U-NetRiver and the one obtained from the hydraulic model was 2.7 m. This result suggests a 29% improvement in prediction of the maximum flood depth in the river.



中文翻译:

一种预测河流洪水深度和范围的深度学习模型

本文提出了一种创新的深度学习 (DL) 框架,以 (a) 自动识别河流几何形状和洪水范围,以及 (b) 预测河流洪水深度。为此,对高级卷积神经网络 (CNN) U-Net 进行了修改,并指定了 U-Net River 名称。修改后,模型接收到一个输入合成图像,包含地面高程和洪水流量两个波段,输出为水深。该模型根据 iRIC(二维水力模型)的输出进行训练和验证,适用于犹他州格林河的一段。结果表明,U-Net River可以自动识别洪水地区的河流形状和湿润面积。U-Net得到的预测河流深度的最大差异河流和从水力模型中获得的为 2.7 m。这一结果表明,河流最大洪水深度的预测提高了 29%。

更新日期:2021-09-04
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