Articles | Volume 23, issue 2
https://doi.org/10.5194/nhess-23-809-2023
https://doi.org/10.5194/nhess-23-809-2023
Research article
 | 
24 Feb 2023
Research article |  | 24 Feb 2023

Transferability of data-driven models to predict urban pluvial flood water depth in Berlin, Germany

Omar Seleem, Georgy Ayzel, Axel Bronstert, and Maik Heistermann

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Cited articles

Adiba, A., Hajji, H., and Maatouk, M.: Transfer learning and U-Net for buildings segmentation, in: Proceedings of the New Challenges in Data Sciences: Acts of the Second Conference of the Moroccan Classification Society, Kenitra Morocco, 28–29 March 2019, Association for Computing Machinery, New York, NY, United States, 1–6, ISBN 978-1-4503-6129-3, 2019. a
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Berghäuser, L., Schoppa, L., Ulrich, J., Dillenardt, L., Jurado, O. E., Passow, C., Mohor, G. S., Seleem, O., Petrow, T., and Thieken, A. H.: Starkregen in Berlin: Meteorologische Ereignisrekonstruktion und Betroffenenbefragung, task force report, University of Potsdam, 44 pp., https://doi.org/10.25932/publishup-50056, 2021. a, b, c
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Short summary
Data-driven models are becoming more of a surrogate that overcomes the limitations of the computationally expensive 2D hydrodynamic models to map urban flood hazards. However, the model's ability to generalize outside the training domain is still a major challenge. We evaluate the performance of random forest and convolutional neural networks to predict urban floodwater depth and investigate their transferability outside the training domain.
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