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| Titel |
Decision-tree analysis of factors influencing rainfall-related building structure and content damage |
| VerfasserIn |
M. H. Spekkers, M. Kok, F. H. L. R. Clemens, J. A. E. ten Veldhuis |
| 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 Sciences ; 14, no. 9 ; Nr. 14, no. 9 (2014-09-24), S.2531-2547 |
| Datensatznummer |
250118669
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| Publikation (Nr.) |
 copernicus.org/nhess-14-2531-2014.pdf |
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| Zusammenfassung |
Flood-damage prediction models are essential building blocks in
flood risk assessments. So far, little research has been dedicated to
damage from small-scale urban floods caused by heavy rainfall, while
there is a need for reliable damage models for this flood type among
insurers and water authorities.
The aim of this paper is to investigate a wide range of
damage-influencing factors and their relationships with
rainfall-related damage, using decision-tree analysis. For this,
district-aggregated claim data from private property insurance
companies in the Netherlands were analysed, for the period 1998–2011.
The databases include claims of water-related damage (for example, damages related to rainwater intrusion through roofs
and pluvial flood water entering buildings at ground floor). Response
variables being modelled are average claim size and claim frequency,
per district, per day. The set of predictors include rainfall-related
variables derived from weather radar images, topographic variables
from a digital terrain model, building-related variables and
socioeconomic indicators of households.
Analyses were made separately for property and content damage claim
data. Results of decision-tree analysis show that claim frequency is
most strongly associated with maximum hourly rainfall intensity,
followed by real estate value, ground floor area, household income,
season (property data only), buildings age (property data only),
a fraction of homeowners (content data only), a and fraction of low-rise
buildings (content data only). It was not possible to develop statistically
acceptable trees for average claim size. It is recommended to investigate
explanations for the failure to derive models. These require the inclusion
of other explanatory factors that were not used in the present study, an
investigation of the variability in average claim size at different spatial
scales, and the collection of more detailed insurance data that allows one to
distinguish between the effects of various damage mechanisms to claim size.
Cross-validation results show that decision trees were able
to predict 22–26% of variance in claim frequency, which is
considerably better compared to results from global multiple
regression models (11–18% of variance explained). Still,
a large part of the variance in claim frequency is left unexplained,
which is likely to be caused by variations in data at subdistrict
scale and missing explanatory variables. |
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