multi-variate flood damage assessment: a tree-based data-mining approach
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ID: 184422
2013
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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.
| Reference Key |
merz2013naturalmulti-variate
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|---|---|
| Authors | ;B. Merz;H. Kreibich;U. Lall |
| Journal | anziam journal |
| Year | 2013 |
| DOI |
10.5194/nhess-13-53-2013
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