TY  - JOUR
A1  - Seleem, Omar
A1  - Ayzel, Georgy
A1  - Costa Tomaz de Souza, Arthur
A1  - Bronstert, Axel
A1  - Heistermann, Maik
T1  - Towards urban flood susceptibility mapping using data-driven models in Berlin, Germany
JF  - Geomatics, natural hazards and risk
N2  - 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 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.
KW  - Urban pluvial flood susceptibility
KW  - convolutional neural network
KW  - deep
KW  - learning
KW  - random forest
KW  - support vector machine
KW  - spatial resolution;
KW  - flood predictors
Y1  - 2022
U6  - https://doi.org/10.1080/19475705.2022.2097131
SN  - 1947-5705
SN  - 1947-5713
VL  - 13
IS  - 1
SP  - 1640
EP  - 1662
PB  - Taylor & Francis
CY  - London
ER  -