@techreport{BerghaeuserSchoppaUlrichetal.2021,
  author    = {Bergh{\"a}user, Lisa and Schoppa, Lukas and Ulrich, Jana and Dillenardt, Lisa and Jurado, Oscar E. and Passow, Christian and Samprogna Mohor, Guilherme and Seleem, Omar and Petrow, Theresia and Thieken, Annegret},
  title     = {Starkregen in Berlin},
  doi       = {10.25932/publishup-50056},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-500560},
  pages     = {44},
  year      = {2021},
  abstract  = {In den Sommern der Jahre 2017 und 2019 kam es in Berlin an mehreren Orten zu {\"U}berschwemmungen in Folge von Starkregenereignissen. In beiden Jahren f{\"u}hrte dies zu erheblichen Beeintr{\"a}chtigungen im Alltag der Berliner:innen sowie zu hohen Sachsch{\"a}den. Eine interdisziplin{\"a}re Taskforce des DFG-Graduiertenkollegs NatRiskChange untersuchte (1) die meteorologischen Eigenschaften zweier besonders eindr{\"u}cklicher Unwetter, sowie (2) die Vulnerabilit{\"a}t der Berliner Bev{\"o}lkerung gegen{\"u}ber Starkregen. Eine vergleichende meteorologische Rekonstruktion der Starkregenereignisse von 2017 und 2019 ergab deutliche Unterschiede in der Entstehung und den {\"U}berschreitungswahrscheinlichkeiten der beiden Unwetter. So war das Ereignis von 2017 mit einer relativ großen r{\"a}umlichen Ausdehnung und langer Dauer ein untypisches Starkregenereignis, w{\"a}hrend es sich bei dem Unwetter von 2019 um ein typisches, kurzzeitiges Starkregenereignis mit ausgepr{\"a}gter r{\"a}umlicher Heterogenit{\"a}t handelte. Eine anschließende statistische Analyse zeigte, dass das Ereignis von 2017 f{\"u}r l{\"a}ngere Niederschlagsdauern (>=24 h) als großfl{\"a}chiges Extremereignis mit {\"U}berschreitungswahrscheinlichkeiten von unter 1 \% einzuordnen ist (d.h. Wiederkehrperioden >=100 Jahre). Im Jahr 2019 wurden dagegen {\"a}hnliche {\"U}berschreitungswahrscheinlichkeiten nur lokal und f{\"u}r k{\"u}rzere Zeitr{\"a}ume (1-2 h) berechnet. Die Vulnerabilit{\"a}tsanalyse basiert auf einer von April bis Juni 2020 in Berlin durchgef{\"u}hrten Onlinebefragung. Diese richtete sich an Personen, die bereits von vergangenen Starkregenereignissen betroffen waren und thematisierte das Schadensereignis selbst, daraus entstandene Beeintr{\"a}chtigungen und Sch{\"a}den, Risikowahrnehmung sowie Notfall- und Vorsorgemaßnahmen. Die erhobenen Umfragedaten (n=102) beziehen sich vornehmlich auf die Ereignisse von 2017 und 2019 und zeigen, dass die Berliner Bev{\"o}lkerung sowohl im Alltag (z.B. bei der Beschaffung von Lebensmitteln) als auch im eigenen Haushalt (z.B. durch {\"U}berschwemmungssch{\"a}den) von den Unwettern beeintr{\"a}chtigt war. Zudem deuteten die Antworten der Betroffenen auf M{\"o}glichkeiten hin, die Vulnerabilit{\"a}t der Gesellschaft gegen{\"u}ber Starkregen weiter zu reduzieren - etwa durch die Unterst{\"u}tzung besonders betroffener Gruppen (z.B. Pflegende), durch gezielte Informationskampagnen zum Schutz vor Starkregen oder durch die Erh{\"o}hung der Reichweite von Unwetterwarnungen. Eine statistische Analyse zur Effektivit{\"a}t privater Notfall- und Vorsorgemaßnahmen auf Grundlage der Umfragedaten best{\"a}tigte vorherige Studienergebnisse. So gab es Anhaltspunkte daf{\"u}r, dass durch das Umsetzen von Vorsorgemaßnahmen wie beispielsweise das Installieren von R{\"u}ckstauklappen, Barriere-Systemen oder Pumpen Starkregensch{\"a}den reduziert werden k{\"o}nnen. Die Ergebnisse dieses Berichts unterstreichen die Notwendigkeit f{\"u}r ein integriertes Starkregenrisikomanagment, das die Risikokomponenten Gef{\"a}hrdung, Vulnerabilit{\"a}t und Exposition ganzheitlich und auf mehreren Ebenen (z.B. staatlich, kommunal, privat) betrachtet.},
  language  = {de}
}
@phdthesis{Seleem2023,
  author    = {Seleem, Omar},
  title     = {Towards urban pluvial flood mapping using data-driven models},
  doi       = {10.25932/publishup-59813},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-598137},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xv, 95},
  year      = {2023},
  abstract  = {Casualties and damages from urban pluvial flooding are increasing. Triggered by short, localized, and intensive rainfall events, urban pluvial floods can occur anywhere, even in areas without a history of flooding. Urban pluvial floods have relatively small temporal and spatial scales. Although cumulative losses from urban pluvial floods are comparable, most flood risk management and mitigation strategies focus on fluvial and coastal flooding. Numerical-physical-hydrodynamic models are considered the best tool to represent the complex nature of urban pluvial floods; however, they are computationally expensive and time-consuming. These sophisticated models make large-scale analysis and operational forecasting prohibitive. Therefore, it is crucial to evaluate and benchmark the performance of other alternative methods. The findings of this cumulative thesis are represented in three research articles. The first study evaluates two topographic-based methods to map urban pluvial flooding, fill-spill-merge (FSM) and topographic wetness index (TWI), by comparing them against a sophisticated hydrodynamic model. The FSM method identifies flood-prone areas within topographic depressions while the TWI method employs maximum likelihood estimation to calibrate a TWI threshold (τ) based on inundation maps from the 2D hydrodynamic model. The results point out that the FSM method outperforms the TWI method. The study highlights then the advantage and limitations of both methods. Data-driven models provide a promising alternative to computationally expensive hydrodynamic models. However, the literature lacks benchmarking studies to evaluate the different models' performance, advantages and limitations. Model transferability in space is a crucial problem. Most studies focus on river flooding, likely due to the relative availability of flow and rain gauge records for training and validation. Furthermore, they consider these models as black boxes. The second study uses a flood inventory for the city of Berlin and 11 predictive features which potentially indicate an increased pluvial flooding hazard to map urban pluvial flood susceptibility using a convolutional neural network (CNN), an artificial neural network (ANN) and the benchmarking machine learning models random forest (RF) and support vector machine (SVM). I investigate the influence of spatial resolution on the implemented models, the models' transferability in space and the importance of the predictive features. The results show that all models perform well and the RF models are superior to the other models within and outside the training domain. The models developed using fine spatial resolution (2 and 5 m) could better identify flood-prone areas. Finally, the results point out that aspect is the most important predictive feature for the CNN models, and altitude is for the other models. While flood susceptibility maps identify flood-prone areas, they do not represent flood variables such as velocity and depth which are necessary for effective flood risk management. To address this, the third study investigates data-driven models' transferability to predict urban pluvial floodwater depth and the models' ability to enhance their predictions using transfer learning techniques. It compares the performance of RF (the best-performing model in the previous study) and CNN models using 12 predictive features and output from a hydrodynamic model. The findings in the third study suggest that while CNN models tend to generalise and smooth the target function on the training dataset, RF models suffer from overfitting. Hence, RF models are superior for predictions inside the training domains but fail outside them while CNN models could control the relative loss in performance outside the training domains. Finally, the CNN models benefit more from transfer learning techniques than RF models, boosting their performance outside training domains. In conclusion, this thesis has evaluated both topographic-based methods and data-driven models to map urban pluvial flooding. However, further studies are crucial to have methods that completely overcome the limitation of 2D hydrodynamic models.},
  language  = {en}
}
@misc{SeleemAyzelBronstertetal.2023,
  author    = {Seleem, Omar and Ayzel, Georgy and Bronstert, Axel and Heistermann, Maik},
  title     = {Transferability of data-driven models to predict urban pluvial flood water depth in Berlin, Germany},
  series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {1323},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-58916},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-589168},
  pages     = {809 -- 822},
  year      = {2023},
  abstract  = {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.},
  language  = {en}
}
@article{SeleemAyzelBronstertetal.2023,
  author    = {Seleem, Omar and Ayzel, Georgy and Bronstert, Axel and Heistermann, Maik},
  title     = {Transferability of data-driven models to predict urban pluvial flood water depth in Berlin, Germany},
  series = {Natural Hazards and Earth System Sciences},
  volume    = {23},
  journal   = {Natural Hazards and Earth System Sciences},
  number    = {2},
  publisher = {Copernicus},
  address   = {G{\"o}ttingen},
  issn      = {1684-9981},
  doi       = {10.5194/nhess-23-809-2023},
  pages     = {809 -- 822},
  year      = {2023},
  abstract  = {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.},
  language  = {en}
}
@misc{SeleemAyzelCostaTomazdeSouzaetal.2022,
  author    = {Seleem, Omar and Ayzel, Georgy and Costa Tomaz de Souza, Arthur and Bronstert, Axel and Heistermann, Maik},
  title     = {Towards urban flood susceptibility mapping using data-driven models in Berlin, Germany},
  series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {1297},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-57680},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-576806},
  pages     = {1640 -- 1662},
  year      = {2022},
  abstract  = {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.},
  language  = {en}
}
@article{SeleemAyzelCostaTomazdeSouzaetal.2022,
  author    = {Seleem, Omar and Ayzel, Georgy and Costa Tomaz de Souza, Arthur and Bronstert, Axel and Heistermann, Maik},
  title     = {Towards urban flood susceptibility mapping using data-driven models in Berlin, Germany},
  series = {Geomatics, natural hazards and risk},
  volume    = {13},
  journal   = {Geomatics, natural hazards and risk},
  number    = {1},
  publisher = {Taylor \& Francis},
  address   = {London},
  issn      = {1947-5705},
  doi       = {10.1080/19475705.2022.2097131},
  pages     = {1640 -- 1662},
  year      = {2022},
  abstract  = {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.},
  language  = {en}
}
@misc{SeleemHeistermannBronstert2021,
  author    = {Seleem, Omar and Heistermann, Maik and Bronstert, Axel},
  title     = {Efficient Hazard Assessment For Pluvial Floods In Urban Environments},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {18},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-52215},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-522158},
  pages     = {19},
  year      = {2021},
  abstract  = {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.},
  language  = {en}
}
@article{SeleemHeistermannBronstert2021,
  author    = {Seleem, Omar and Heistermann, Maik and Bronstert, Axel},
  title     = {Efficient Hazard Assessment For Pluvial Floods In Urban Environments},
  series = {Water},
  volume    = {13},
  journal   = {Water},
  number    = {18},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {2073-4441},
  doi       = {10.3390/w13182476},
  pages     = {17},
  year      = {2021},
  abstract  = {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.},
  language  = {en}
}