@article{KreibichBottoMerzetal.2016,
  author    = {Kreibich, Heidi and Botto, Anna and Merz, Bruno and Schr{\"o}ter, Kai},
  title     = {Probabilistic, Multivariable Flood Loss Modeling on the Mesoscale with BT-FLEMO},
  series = {Risk analysis},
  volume    = {37},
  journal   = {Risk analysis},
  number    = {4},
  publisher = {Wiley},
  address   = {Hoboken},
  issn      = {0272-4332},
  doi       = {10.1111/risa.12650},
  pages     = {774 -- 787},
  year      = {2016},
  abstract  = {Flood loss modeling is an important component for risk analyses and decision support in flood risk management. Commonly, flood loss models describe complex damaging processes by simple, deterministic approaches like depth-damage functions and are associated with large uncertainty. To improve flood loss estimation and to provide quantitative information about the uncertainty associated with loss modeling, a probabilistic, multivariable Bagging decision Tree Flood Loss Estimation MOdel (BT-FLEMO) for residential buildings was developed. The application of BT-FLEMO provides a probability distribution of estimated losses to residential buildings per municipality. BT-FLEMO was applied and validated at the mesoscale in 19 municipalities that were affected during the 2002 flood by the River Mulde in Saxony, Germany. Validation was undertaken on the one hand via a comparison with six deterministic loss models, including both depth-damage functions and multivariable models. On the other hand, the results were compared with official loss data. BT-FLEMO outperforms deterministic, univariable, and multivariable models with regard to model accuracy, although the prediction uncertainty remains high. An important advantage of BT-FLEMO is the quantification of prediction uncertainty. The probability distribution of loss estimates by BT-FLEMO well represents the variation range of loss estimates of the other models in the case study.},
  language  = {en}
}
@article{SiegVogelMerzetal.2019,
  author    = {Sieg, Tobias and Vogel, Kristin and Merz, Bruno and Kreibich, Heidi},
  title     = {Seamless Estimation of Hydrometeorological Risk Across Spatial Scales},
  series = {Earth's Future},
  volume    = {7},
  journal   = {Earth's Future},
  number    = {5},
  publisher = {Wiley-Blackwell},
  address   = {Hoboken, NJ},
  issn      = {2328-4277},
  doi       = {10.1029/2018EF001122},
  pages     = {574 -- 581},
  year      = {2019},
  abstract  = {Hydrometeorological hazards caused losses of approximately 110 billion U.S. Dollars in 2016 worldwide. Current damage estimations do not consider the uncertainties in a comprehensive way, and they are not consistent between spatial scales. Aggregated land use data are used at larger spatial scales, although detailed exposure data at the object level, such as openstreetmap.org, is becoming increasingly available across the globe.We present a probabilistic approach for object-based damage estimation which represents uncertainties and is fully scalable in space. The approach is applied and validated to company damage from the flood of 2013 in Germany. Damage estimates are more accurate compared to damage models using land use data, and the estimation works reliably at all spatial scales. Therefore, it can as well be used for pre-event analysis and risk assessments. This method takes hydrometeorological damage estimation and risk assessments to the next level, making damage estimates and their uncertainties fully scalable in space, from object to country level, and enabling the exploitation of new exposure data.},
  language  = {en}
}