TY  - JOUR
A1  - Seleem, Omar
A1  - Ayzel, Georgy
A1  - Bronstert, Axel
A1  - Heistermann, Maik
T1  - Transferability of data-driven models to predict urban pluvial flood water depth in Berlin, Germany
JF  - Natural Hazards and Earth System Sciences
N2  - Data-driven models have been recently suggested to surrogate computationally expensive hydrodynamic models to map flood hazards. However, most studies focused on developing models for the same area or the same precipitation event. It is thus not obvious how transferable the models are in space. This study evaluates the performance of a convolutional neural network (CNN) based on the U-Net architecture and the random forest (RF) algorithm to predict flood water depth, the models' transferability in space and performance improvement using transfer learning techniques. We used three study areas in Berlin to train, validate and test the models. The results showed that (1) the RF models outperformed the CNN models for predictions within the training domain, presumable at the cost of overfitting; (2) the CNN models had significantly higher potential than the RF models to generalize beyond the training domain; and (3) the CNN models could better benefit from transfer learning technique to boost their performance outside training domains than RF models.
Y1  - 2023
U6  - https://doi.org/10.5194/nhess-23-809-2023
SN  - 1684-9981
SN  - 1561-8633
VL  - 23
IS  - 2
SP  - 809
EP  - 822
PB  - Copernicus
CY  - Göttingen
ER  - 
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  - 
TY  - JOUR
A1  - Seleem, Omar
A1  - Heistermann, Maik
A1  - Bronstert, Axel
T1  - Efficient Hazard Assessment For Pluvial Floods In Urban Environments
BT  - A Benchmarking Case Study For The City Of Berlin, Germany
JF  - Water
N2  - The presence of impermeable surfaces in urban areas hinders natural drainage and directs the surface runoff to storm drainage systems with finite capacity, which makes these areas prone to pluvial flooding. The occurrence of pluvial flooding depends on the existence of minimal areas for surface runoff generation and concentration. Detailed hydrologic and hydrodynamic simulations are computationally expensive and require intensive resources. This study compared and evaluated the performance of two simplified methods to identify urban pluvial flood-prone areas, namely the fill–spill–merge (FSM) method and the topographic wetness index (TWI) method and used the TELEMAC-2D hydrodynamic numerical model for benchmarking and validation. The FSM method uses common GIS operations to identify flood-prone depressions from a high-resolution digital elevation model (DEM). The TWI method employs the maximum likelihood method (MLE) to probabilistically calibrate a TWI threshold (τ) based on the inundation maps from a 2D hydrodynamic model for a given spatial window (W) within the urban area. We found that the FSM method clearly outperforms the TWI method both conceptually and effectively in terms of model performance.
KW  - urban pluvial flooding
KW  - digital elevation model (DEM)
KW  - fill–spill–merge method
KW  - topographic wetness index (TWI)
KW  - TELEMAC-2D model
KW  - flood-prone area
Y1  - 2021
U6  - https://doi.org/10.3390/w13182476
SN  - 2073-4441
VL  - 13
IS  - 18
PB  - MDPI
CY  - Basel
ER  -