Journal cover Journal topic
Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
doi:10.5194/nhess-2017-7
© Author(s) 2017. This work is distributed
under the Creative Commons Attribution 3.0 License.
Research article
12 Jan 2017
Review status
This discussion paper is under review for the journal Natural Hazards and Earth System Sciences (NHESS).
Data-mining for multi-variable flood damage modelling with limited data
Dennis Wagenaar, Jurjen de Jong, and Laurens M. Bouwer Deltares, Delft, The Netherlands
Abstract. Flood damage assessment is usually done with damage curves only dependent on the water depth. Recent studies have shown that data-mining techniques applied to a multi-dimensional dataset can produce significantly better flood damage estimates. However, creating and applying a multi-variable flood damage model requires an extensive dataset, which is rarely available and this can limit the application of these new techniques. In this paper we enrich a dataset of residential building and content damages from the Meuse flood of 1993 in the Netherlands, to make it suitable for multi-variable flood damage assessment. Results from 2D flood simulations are used to add information on flow velocity, flood duration and the return period to the dataset, and cadastre data is used to add information on building characteristics. Next, several statistical approaches are used to create multi-variable flood damage models, including regression trees, bagging regression trees, random forest, and a Bayesian network. Validation on data points from a test set shows that the enriched dataset in combination with the data-mining techniques delivers a significant improvement over a simple model only based on the water depth. We find that with our dataset, the trees based methods perform better than the Bayesian Network.

Citation: Wagenaar, D., de Jong, J., and Bouwer, L. M.: Data-mining for multi-variable flood damage modelling with limited data, Nat. Hazards Earth Syst. Sci. Discuss., doi:10.5194/nhess-2017-7, in review, 2017.
Dennis Wagenaar et al.
Dennis Wagenaar et al.
Dennis Wagenaar et al.

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Short summary
Flood damage models are an important component of cost-benefit analyses for flood protection measures. Currently flood damage models predict the flood damage only based on the water depth. Recently, some progress has been made in also including other variables that can help predict the flood damage. Data-intensive approaches (machine learning) have been applied to do this. In practice the required data is rare. We apply these new approaches on a different type of dataset.
Flood damage models are an important component of cost-benefit analyses for flood protection...
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