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

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 l...

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Bibliographic Details
Published in:Risk analysis 2017-04, Vol.37 (4), p.774-787
Main Authors: Kreibich, Heidi, Botto, Anna, Merz, Bruno, Schröter, Kai
Format: Article
Language:English
Subjects:
Buildings
Damage modeling
Data processing
Decision analysis
Flood control
Flood damage
Flood insurance
Flood management
Mathematical models
Measurement
Modelling
multiparameter
Municipalities
probabilistic
Probability
Probability distribution
Residential buildings
Risk assessment
Risk management
Rivers
Uncertainty
validation
Validity
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Summary: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.
ISSN:0272-4332
1539-6924
DOI:10.1111/risa.12650