Abstract. Flood damage assessment is usually done with damage curves only dependent on the water depth. Several recent studies have shown that supervised learning techniques applied to a multi-variable data set can produce significantly better flood damage estimates. However, creating and applying a multi-variable flood damage model requires an extensive data set, which is rarely available, and this is currently holding back the widespread application of these techniques. In this paper we enrich a data set of residential building and contents damage from the Meuse flood of 1993 in the Netherlands, to make it suitable for multi-variable flood damage assessment. Results from 2-D flood simulations are used to add information on flow velocity, flood duration and the return period to the data set, and cadastre data are 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 data set in combination with the supervised learning techniques delivers a 20 % reduction in the mean absolute error, compared to a simple model only based on the water depth, despite several limitations of the enriched data set. We find that with our data set, the tree-based methods perform better than the Bayesian network.

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 10 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, 15 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. IntroductionBecause flood risk management becomes increasingly risk-based, flood damage estimation is increasingly important in flood 20 risk assessment. Flood risk assessment supports policy makers to decide which flood risk management measures are most efficient in reducing flood risks and how much investment is cost-efficient. With the European Union Floods Directive (EC, 2007) now fully in place, national flood risk assessment are being developed with the final aim to support flood risk management plans. In the Netherlands, such flood damage assessment has been used to derive the optimal protection standard for flood protection (Kind, 2013; van der Most, 2014), using the current Dutch standard method for damage 25 modelling (Kok et al., 2005). Also for insurance applications, more precise estimates of flood damages are required.Flood risk assessments require flood damage models. These models typically predict the fraction of damage based on the water depth, and average building repair and replacement costs for different types of buildings (Messner et al., 2007;Jonkman et al., 2008). When validated, such simple flood damage models often don't perform well (e.g. Jongman et al., 2012). This is because water depth alone cannot explain the full complexity of the flood damaging processes and several 30 studies have only found low correlation coefficients (typically below 0.5) between the water depth and the flood damage (e.g. Merz et al., 2013, Pistrika&Jonkman, 2009). Furthermore, often no local data is available on flood damage and Nat. Hazards Earth Syst. Sci. Discuss.,