U-FLOOD – Topographic deep learning for predicting urban pluvial flood water depth

[Display omitted] •Predicting urban pluvial flood depth using deep learning.•Generates flood predictions for areas and rain events where U-FLOOD was not trained.•Systematic assessment of required spatial inputs for optimal predictions.•Sensitivity assessment of neural network hyperparameters. This s...

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Bibliographic Details
Published in:Journal of hydrology (Amsterdam) 2021-12, Vol.603, p.126898, Article 126898
Main Authors: Löwe, Roland, Böhm, Julian, Jensen, David Getreuer, Leandro, Jorge, Rasmussen, Søren Højmark
Format: Article
Language:English
Subjects:
Convolutional neural network
data collection
Deep learning
Emulation
Hydrodynamic modelling
hydrology
image analysis
landscapes
neural networks
prediction
rain
sewer systems
topography
Urban pluvial flooding
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