Articles | Volume 13, issue 1
Nat. Hazards Earth Syst. Sci., 13, 53–64, 2013

Special issue: Costs of Natural Hazards

Nat. Hazards Earth Syst. Sci., 13, 53–64, 2013

Research article 11 Jan 2013

Research article | 11 Jan 2013

Multi-variate flood damage assessment: a tree-based data-mining approach

B. Merz1, H. Kreibich1, and U. Lall2 B. Merz et al.
  • 1GFZ German Research Centre for Geosciences, Section 5.4, 14473 Potsdam, Germany
  • 2Columbia University, Department of Earth & Environmental Engineering, New York, NY 10027, USA

Abstract. The usual approach for flood damage assessment consists of stage-damage functions which relate the relative or absolute damage for a certain class of objects to the inundation depth. Other characteristics of the flooding situation and of the flooded object are rarely taken into account, although flood damage is influenced by a variety of factors. We apply a group of data-mining techniques, known as tree-structured models, to flood damage assessment. A very comprehensive data set of more than 1000 records of direct building damage of private households in Germany is used. Each record contains details about a large variety of potential damage-influencing characteristics, such as hydrological and hydraulic aspects of the flooding situation, early warning and emergency measures undertaken, state of precaution of the household, building characteristics and socio-economic status of the household. Regression trees and bagging decision trees are used to select the more important damage-influencing variables and to derive multi-variate flood damage models. It is shown that these models outperform existing models, and that tree-structured models are a promising alternative to traditional damage models.

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