Literature DB >> 27612204

Probabilistic, Multivariable Flood Loss Modeling on the Mesoscale with BT-FLEMO.

Heidi Kreibich1, Anna Botto2, Bruno Merz1,3, Kai Schröter1.   

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.
© 2016 Society for Risk Analysis.

Entities:  

Keywords:  Damage modeling; multiparameter; probabilistic; uncertainty; validation

Year:  2016        PMID: 27612204     DOI: 10.1111/risa.12650

Source DB:  PubMed          Journal:  Risk Anal        ISSN: 0272-4332            Impact factor:   4.000


  1 in total

1.  Integrated assessment of short-term direct and indirect economic flood impacts including uncertainty quantification.

Authors:  Tobias Sieg; Thomas Schinko; Kristin Vogel; Reinhard Mechler; Bruno Merz; Heidi Kreibich
Journal:  PLoS One       Date:  2019-04-04       Impact factor: 3.240

  1 in total

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