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 inventory (4333 flooded locations) and 11 factors which potentially indicate an increased hazard for pluvial flooding were used to implement convolutional neural network (CNN), artificial neural network (ANN), random forest (RF) and support vector machine (SVM) to: (1) Map flood susceptibility in Berlin at 30, 10, 5, and 2 m spatial resolutions. (2) Evaluate the trained models' transferability in space. (3) Estimate the most useful factors for flood susceptibility mapping. The models' performance was validated using the Kappa, and the area under the receiver operating characteristic curve (AUC). The results indicated that all models perform very well (minimum AUC = 0.87 for the testing dataset). The RF models outperformed all other models at allIdentifying 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 inventory (4333 flooded locations) and 11 factors which potentially indicate an increased hazard for pluvial flooding were used to implement convolutional neural network (CNN), artificial neural network (ANN), random forest (RF) and support vector machine (SVM) to: (1) Map flood susceptibility in Berlin at 30, 10, 5, and 2 m spatial resolutions. (2) Evaluate the trained models' transferability in space. (3) Estimate the most useful factors for flood susceptibility mapping. The models' performance was validated using the Kappa, and the area under the receiver operating characteristic curve (AUC). The results indicated that all models perform very well (minimum AUC = 0.87 for the testing dataset). The RF models outperformed all other models at all spatial resolutions and the RF model at 2 m spatial resolution was superior for the present flood inventory and predictor variables. The majority of the models had a moderate performance for predictions outside the training area based on Kappa evaluation (minimum AUC = 0.8). Aspect and altitude were the most influencing factors on the image-based and point-based models respectively. Data-driven models can be a reliable tool for urban pluvial flood susceptibility mapping wherever a reliable flood inventory is available.…
Author details: | Omar SeleemORCiDGND, Georgy AyzelORCiDGND, Arthur Costa Tomaz de SouzaORCiD, Axel BronstertORCiDGND, Maik HeistermannORCiDGND |
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DOI: | https://doi.org/10.1080/19475705.2022.2097131 |
ISSN: | 1947-5705 |
ISSN: | 1947-5713 |
Title of parent work (English): | Geomatics, natural hazards and risk |
Publisher: | Taylor & Francis |
Place of publishing: | London |
Publication type: | Article |
Language: | English |
Date of first publication: | 2022/07/12 |
Publication year: | 2022 |
Release date: | 2022/10/20 |
Tag: | Urban pluvial flood susceptibility; convolutional neural network; deep; flood predictors; learning; random forest; spatial resolution;; support vector machine |
Volume: | 13 |
Issue: | 1 |
Number of pages: | 23 |
First page: | 1640 |
Last Page: | 1662 |
Funding institution: | Deutscher Akademischer Austauschdienst (DAAD); Deutsche; Forschungsgemeinschaft; Open Access Publishing Fund of the University of; Potsdam; German Academic Exchange Service |
Organizational units: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Umweltwissenschaften und Geographie |
DDC classification: | 5 Naturwissenschaften und Mathematik / 55 Geowissenschaften, Geologie / 550 Geowissenschaften |
Peer review: | Referiert |
Publishing method: | Open Access / Gold Open-Access |
License (German): | CC-BY - Namensnennung 4.0 International |
External remark: | Zweitveröffentlichung in der Schriftenreihe Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe ; 1297 |