Towards urban flood susceptibility mapping using data-driven models in Berlin, Germany

Towards urban flood susceptibility mapping using data-driven models in Berlin, Germany

Identifying urban pluvial flood-prone areas is necessary but the application of two-dimensional hydrodynamic models is limited to small areas. Data-driven models have been showing their ability to map flood susceptibility but their application in urban pluvial flooding is still rare. A flood invento...

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Bibliographic Details
Main Authors: Omar Seleem, Georgy Ayzel, Arthur Costa Tomaz de Souza, Axel Bronstert, Maik Heistermann
Format: Article
Language:English
Published: Taylor & Francis Group 2022-12-01
Series:Geomatics, Natural Hazards & Risk
Subjects:
Urban pluvial flood susceptibility
convolutional neural network
deep learning
random forest
support vector machine
spatial resolution
Online Access:https://www.tandfonline.com/doi/10.1080/19475705.2022.2097131

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https://www.tandfonline.com/doi/10.1080/19475705.2022.2097131

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