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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Principais autores:	Omar Seleem, Georgy Ayzel, Arthur Costa Tomaz de Souza, Axel Bronstert, Maik Heistermann
格式:	Artigo
语言:	Inglês
出版:	Taylor & Francis Group 2022-12-01
丛编:	Geomatics, Natural Hazards & Risk
主题:	Urban pluvial flood susceptibility convolutional neural network deep learning random forest support vector machine spatial resolution
在线阅读:	https://www.tandfonline.com/doi/10.1080/19475705.2022.2097131
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