 |
| Titel |
Bayesian network learning for natural hazard analyses |
| VerfasserIn |
K. Vogel, C. Riggelsen, O. Korup, F. Scherbaum |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1561-8633
|
| Digitales Dokument |
URL |
| Erschienen |
In: Natural Hazards and Earth System Sciences ; 14, no. 9 ; Nr. 14, no. 9 (2014-09-29), S.2605-2626 |
| Datensatznummer |
250118673
|
| Publikation (Nr.) |
 copernicus.org/nhess-14-2605-2014.pdf |
|
|
|
|
|
| Zusammenfassung |
Modern natural hazards research requires dealing with several uncertainties
that arise from limited process knowledge, measurement errors, censored and
incomplete observations, and the intrinsic randomness of the governing
processes. Nevertheless, deterministic analyses are still widely used in
quantitative hazard assessments despite the pitfall of misestimating the
hazard and any ensuing risks.
In this paper we show that Bayesian networks offer a flexible framework for
capturing and expressing a broad range of uncertainties encountered in
natural hazard assessments. Although Bayesian networks are well studied in
theory, their application to real-world data is far from straightforward, and
requires specific tailoring and adaptation of existing algorithms. We offer
suggestions as how to tackle frequently arising problems in this context and
mainly concentrate on the handling of continuous variables, incomplete data
sets, and the interaction of both. By way of three case studies from
earthquake, flood, and landslide research, we demonstrate the method of
data-driven Bayesian network learning, and showcase the flexibility,
applicability, and benefits of this approach.
Our results offer fresh and partly counterintuitive insights into
well-studied multivariate problems of earthquake-induced ground motion
prediction, accurate flood damage quantification, and spatially explicit
landslide prediction at the regional scale. In particular, we highlight how
Bayesian networks help to express information flow and independence
assumptions between candidate predictors. Such knowledge is pivotal in
providing scientists and decision makers with well-informed strategies for
selecting adequate predictor variables for quantitative natural hazard
assessments. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|