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| Titel |
Multi-variate flood damage assessment: a tree-based data-mining approach |
| VerfasserIn |
B. Merz, H. Kreibich, U. Lall |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1561-8633
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| Digitales Dokument |
URL |
| Erschienen |
In: Natural Hazards and Earth System Science ; 13, no. 1 ; Nr. 13, no. 1 (2013-01-11), S.53-64 |
| Datensatznummer |
250017530
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| Publikation (Nr.) |
 copernicus.org/nhess-13-53-2013.pdf |
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| Zusammenfassung |
| 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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