Applications of Bayesian networks in natural hazard assessments
Even though quite different in occurrence and consequences, from a modeling perspective many natural hazards share similar properties and challenges. Their complex nature as well as lacking knowledge about their driving forces and potential effects make their analysis demanding: uncertainty about th...
Ausführliche Beschreibung
Autor*in: |
Vogel, Kristin [verfasserIn] Scherbaum, Frank [betreuer] |
---|---|
Hochschulschrift: |
Potsdam, Univ., Kumulative Diss., 2014 |
Format: |
E-Book |
---|---|
Sprache: |
Englisch |
Erschienen: |
2014 |
---|
Schlagwörter: | |
---|---|
Formangabe: |
Hochschulschrift |
Umfang: |
Online-Ressource (PDF-Datei: 11441 KB, X, 84 Bl.) |
---|
Weitere Ausgabe: |
Druckausg. Vogel, Kristin: Applications of Bayesian networks in natural hazard assessments - Potsdam, 2013 |
---|
Links: |
---|
DOI / URN: |
urn:nbn:de:kobv:517-opus-69777 |
---|
Katalog-ID: |
782264476 |
---|
LEADER | 01000cam a2200265 4500 | ||
---|---|---|---|
001 | 782264476 | ||
003 | DE-627 | ||
005 | 20230324224533.0 | ||
007 | cr uuu---uuuuu | ||
008 | 140402s2014 gw |||||om 00| ||eng c | ||
015 | |a 14,O04 |2 dnb | ||
016 | 7 | |a 1048476014 |2 DE-101 | |
024 | 7 | |a urn:nbn:de:kobv:517-opus-69777 |2 urn | |
035 | |a (DE-627)782264476 | ||
035 | |a (DE-599)DNB1048476014 | ||
035 | |a (OCoLC)875594439 | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
044 | |c XA-DE-BB | ||
082 | 0 | 4 | |a 550 |q DNB |
084 | |a 43.48 |2 bkl | ||
084 | |a 31.70 |2 bkl | ||
100 | 1 | |a Vogel, Kristin |e verfasserin |0 (DE-588)1074045890 |0 (DE-627)830216421 |0 (DE-576)435559109 |4 aut | |
245 | 1 | 0 | |a Applications of Bayesian networks in natural hazard assessments |c Kristin Vogel. Betreuer: Frank Scherbaum |
246 | 1 | |i Parallelsacht. |a Anwendungen von Bayes'schen Netzen bei der Einschätzung von Naturgefahren | |
264 | 1 | |c 2014 | |
300 | |a Online-Ressource (PDF-Datei: 11441 KB, X, 84 Bl.) | ||
336 | |a Text |b txt |2 rdacontent | ||
337 | |a Computermedien |b c |2 rdamedia | ||
338 | |a Online-Ressource |b cr |2 rdacarrier | ||
502 | |a Potsdam, Univ., Kumulative Diss., 2014 | ||
520 | |a Even though quite different in occurrence and consequences, from a modeling perspective many natural hazards share similar properties and challenges. Their complex nature as well as lacking knowledge about their driving forces and potential effects make their analysis demanding: uncertainty about the modeling framework, inaccurate or incomplete event observations and the intrinsic randomness of the natural phenomenon add up to different interacting layers of uncertainty, which require a careful handling. Nevertheless deterministic approaches are still widely used in natural hazard assessments, holding the risk of underestimating the hazard with disastrous effects. The all-round probabilistic framework of Bayesian networks constitutes an attractive alternative. In contrast to deterministic proceedings, it treats response variables as well as explanatory variables as random variables making no difference between input and output variables. Using a graphical representation Bayesian networks encode the dependency relations between the variables in a directed acyclic graph: variables are represented as nodes and (in-)dependencies between variables as (missing) edges between the nodes. The joint distribution of all variables can thus be described by decomposing it, according to the depicted independences, into a product of local conditional probability distributions, which are defined by the parameters of the Bayesian network. In the framework of this thesis the Bayesian network approach is applied to different natural hazard domains (i.e. seismic hazard, flood damage and landslide assessments). Learning the network structure and parameters from data, Bayesian networks reveal relevant dependency relations between the included variables and help to gain knowledge about the underlying processes. The problem of Bayesian network learning is cast in a Bayesian framework, considering the network structure and parameters as random variables itself and searching for the most likely combination of both, which corresponds to the maximum a posteriori (MAP score) of their joint distribution given the observed data. Although well studied in theory the learning of Bayesian networks based on real-world data is usually not straight forward and requires an adoption of existing algorithms. Typically arising problems are the handling of continuous variables, incomplete observations and the interaction of both. Working with continuous distributions requires assumptions about the allowed families of distributions. To "let the data speak" and avoid wrong assumptions, continuous variables are instead discretized here, thus allowing for a completely data-driven and distribution-free learning. An extension of the MAP score, considering the discretization as random variable as well, is developed for an automatic multivariate discretization, that takes interactions between the variables into account. The discretization process is nested into the network learning and requires several iterations. Having to face incomplete observations on top, this may pose a computational burden. Iterative proceedings for missing value estimation become quickly infeasible. A more efficient albeit approximate method is used instead, estimating the missing values based only on the observations of variables directly interacting with the missing variable. Moreover natural hazard assessments often have a primary interest in a certain target variable. The discretization learned for this variable does not always have the required resolution for a good prediction performance. Finer resolutions for (conditional) continuous distributions are achieved with continuous approximations subsequent to the Bayesian network learning, using kernel density estimations or mixtures of truncated exponential functions. All our proceedings are completely data-driven. We thus avoid assumptions that require expert knowledge and instead provide domain independent solutions, that are applicable not only in other natural hazard assessments, but in a variety of domains struggling with uncertainties. | ||
583 | 1 | |z Langzeitarchivierung gewährleistet |2 pdager | |
655 | 7 | |a Hochschulschrift |0 (DE-588)4113937-9 |0 (DE-627)105825778 |0 (DE-576)209480580 |2 gnd-content | |
689 | 0 | 0 | |D s |0 (DE-588)4123823-0 |0 (DE-627)105752304 |0 (DE-576)209562978 |a Naturgefahr |2 gnd |
689 | 0 | 1 | |D s |0 (DE-588)4137042-9 |0 (DE-627)10427932X |0 (DE-576)209674318 |a Risikoanalyse |2 gnd |
689 | 0 | 2 | |D s |0 (DE-588)4567228-3 |0 (DE-627)306094517 |0 (DE-576)213783134 |a Bayes-Netz |2 gnd |
689 | 0 | |5 (DE-627) | |
700 | 1 | |a Scherbaum, Frank |e betreuer |4 oth | |
751 | |a Potsdam |4 uvp | ||
776 | 0 | 8 | |i Druckausg. |a Vogel, Kristin |t Applications of Bayesian networks in natural hazard assessments |d Potsdam, 2013 |h x, 84 Blätter |w (DE-627)830342885 |
856 | 4 | 0 | |u http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 |v 2014-04-02 |x Resolving-System |3 Volltext |
856 | 4 | 0 | |u http://d-nb.info/1048476014/34 |v 2014-04-02 |x Langzeitarchivierung Nationalbibliothek |3 Volltext |
856 | 4 | 0 | |u http://opus.kobv.de/ubp/volltexte/2014/6977/ |v 2014-04-02 |x Verlag |z kostenfrei |3 Volltext |
912 | |a GBV-ODiss | ||
912 | |a GBV_ILN_20 | ||
912 | |a ISIL_DE-84 | ||
912 | |a SYSFLAG_1 | ||
912 | |a GBV_KXP | ||
912 | |a SSG-OPC-GGO | ||
912 | |a GBV_ILN_21 | ||
912 | |a ISIL_DE-46 | ||
912 | |a GBV_ILN_22 | ||
912 | |a ISIL_DE-18 | ||
912 | |a GBV_ILN_23 | ||
912 | |a ISIL_DE-830 | ||
912 | |a GBV_ILN_30 | ||
912 | |a ISIL_DE-104 | ||
912 | |a GBV_ILN_40 | ||
912 | |a ISIL_DE-7 | ||
912 | |a GBV_ILN_60 | ||
912 | |a ISIL_DE-705 | ||
912 | |a GBV_ILN_63 | ||
912 | |a ISIL_DE-Wim2 | ||
912 | |a GBV_ILN_70 | ||
912 | |a ISIL_DE-89 | ||
912 | |a GBV_ILN_105 | ||
912 | |a ISIL_DE-841 | ||
912 | |a GBV_ILN_110 | ||
912 | |a ISIL_DE-Luen4 | ||
912 | |a GBV_ILN_132 | ||
912 | |a ISIL_DE-959 | ||
912 | |a GBV_ILN_151 | ||
912 | |a ISIL_DE-546 | ||
912 | |a GBV_ILN_161 | ||
912 | |a ISIL_DE-960 | ||
912 | |a GBV_ILN_285 | ||
912 | |a ISIL_DE-517 | ||
912 | |a GBV_ILN_293 | ||
912 | |a ISIL_DE-960-3 | ||
912 | |a GBV_ILN_370 | ||
912 | |a ISIL_DE-1373 | ||
936 | b | k | |a 43.48 |j Regionale Umweltprobleme |0 (DE-627)181569981 |
936 | b | k | |a 31.70 |j Wahrscheinlichkeitsrechnung |0 (DE-627)106408070 |
951 | |a BO | ||
980 | |2 20 |1 01 |x 0084 |b 1475518501 |y x |z 29-05-14 | ||
980 | |2 21 |1 01 |x 0046 |b 1475536720 |y z |z 29-05-14 | ||
980 | |2 22 |1 01 |x 0018 |b 1475554664 |h SUBolrd |y xu |z 29-05-14 | ||
980 | |2 23 |1 01 |x 0830 |b 1475568061 |h olr-d |y x |z 29-05-14 | ||
980 | |2 30 |1 01 |x 0104 |b 1475578156 |y z |z 29-05-14 | ||
980 | |2 40 |1 01 |x 0007 |b 1475590288 |y xsn |z 29-05-14 | ||
980 | |2 60 |1 01 |x 0705 |b 1475608004 |h OLRD |y z |z 29-05-14 | ||
980 | |2 63 |1 01 |x 3401 |b 1595845496 |h ORD |y x |z 21-01-16 | ||
980 | |2 70 |1 01 |x 0089 |b 1475634854 |y zdo |z 29-05-14 | ||
980 | |2 105 |1 01 |x 0841 |b 1596657936 |y z |z 21-01-16 | ||
980 | |2 110 |1 01 |x 3110 |b 1475623658 |y x |z 29-05-14 | ||
980 | |2 132 |1 01 |x 0959 |b 1498549047 |h OLR-DISS |y x |z 14-08-14 | ||
980 | |2 151 |1 01 |x 0546 |b 3594257711 |h OLR-ODISS |y z |z 13-02-20 | ||
980 | |2 161 |1 01 |x 0960 |b 1597648639 |h ORD |y x |z 28-01-16 | ||
980 | |2 285 |1 01 |x 0517 |b 1554521785 |c 00 |f 9300 |d --%%-- |e s |j --%%-- |y z |z 21-07-15 | ||
980 | |2 293 |1 01 |x 3293 |b 1598162004 |h ORD |y xf |z 28-01-16 | ||
980 | |2 370 |1 01 |x 4370 |b 1475630204 |y x |z 29-05-14 | ||
981 | |2 20 |1 01 |x 0084 |r http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 | ||
981 | |2 21 |1 01 |x 0046 |r http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 | ||
981 | |2 22 |1 01 |x 0018 |r http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 | ||
981 | |2 23 |1 01 |x 0830 |r http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 | ||
981 | |2 30 |1 01 |x 0104 |r http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 | ||
981 | |2 40 |1 01 |x 0007 |r http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 | ||
981 | |2 60 |1 01 |x 0705 |r http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 | ||
981 | |2 63 |1 01 |x 3401 |y E-Book |r http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 |z LF | ||
981 | |2 70 |1 01 |x 0089 |r http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 | ||
981 | |2 105 |1 01 |x 0841 |r http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 | ||
981 | |2 110 |1 01 |x 3110 |r http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 | ||
981 | |2 132 |1 01 |x 0959 |r http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 | ||
981 | |2 151 |1 01 |x 0546 |y Volltext |r http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 | ||
981 | |2 161 |1 01 |x 0960 |r http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 | ||
981 | |2 285 |1 01 |x 0517 |r http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 | ||
981 | |2 293 |1 01 |x 3293 |r http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 | ||
981 | |2 370 |1 01 |x 4370 |r http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 | ||
983 | |2 60 |1 01 |x 0705 |8 10 |a ho | ||
983 | |2 285 |1 00 |x DE-517 |8 00 |a TF 04999 | ||
983 | |2 285 |1 00 |x DE-517 |8 00 |a AR 14120 | ||
983 | |2 285 |1 00 |x DE-517 |8 00 |a SK 830 | ||
983 | |2 285 |1 00 |x DE-517 |8 00 |a UT 1800 | ||
985 | |2 20 |1 01 |x 0084 |a OLRD | ||
985 | |2 110 |1 01 |x 3110 |a OLRD | ||
985 | |2 370 |1 01 |x 4370 |a OLRD | ||
995 | |2 22 |1 01 |x 0018 |a SUBolrd | ||
995 | |2 23 |1 01 |x 0830 |a olr-d | ||
995 | |2 60 |1 01 |x 0705 |a OLRD | ||
995 | |2 63 |1 01 |x 3401 |a ORD | ||
995 | |2 132 |1 01 |x 0959 |a OLR-DISS | ||
995 | |2 151 |1 01 |x 0546 |a OLR-ODISS | ||
995 | |2 161 |1 01 |x 0960 |a ORD | ||
995 | |2 293 |1 01 |x 3293 |a ORD | ||
998 | |2 23 |1 01 |x 0830 |0 2014-05-29:01:06:56 |
matchkey_str |
vogelkristinscherbaumfrank:2014----:plctosfaeinewrsnauahz |
---|---|
oclc_num |
875594439 |
publishDate |
2014 |
building |
20 21 22:u 23 30 40:s 60 63 70:d 105 110 132 151 161 285 293:f 370 |
topic_facet |
Naturgefahr Risikoanalyse Bayes-Netz |
hochschulschrift_txt_mv |
Potsdam, Univ., Kumulative Diss., 2014 |
isfreeaccess_bool |
true |
publishDateDaySort_date |
2014-01-01T00:00:00Z |
dewey-sort |
3550 |
id |
782264476 |
language_de |
englisch |
standort_str_mv |
9300 |
dewey-ones |
550 - Earth sciences |
delete_txt_mv |
keep |
author_role |
aut |
last_changed_iln_str_mv |
20@29-05-14 21@29-05-14 22@29-05-14 23@29-05-14 30@29-05-14 40@29-05-14 60@29-05-14 63@21-01-16 70@29-05-14 105@21-01-16 110@29-05-14 132@14-08-14 151@13-02-20 161@28-01-16 285@21-07-15 293@28-01-16 370@29-05-14 |
illustrated |
Not Illustrated |
topic_title |
550 DNB 43.48 bkl 31.70 bkl Applications of Bayesian networks in natural hazard assessments Kristin Vogel. Betreuer: Frank Scherbaum s (DE-588)4123823-0 (DE-627)105752304 (DE-576)209562978 Naturgefahr gnd s (DE-588)4137042-9 (DE-627)10427932X (DE-576)209674318 Risikoanalyse gnd s (DE-588)4567228-3 (DE-627)306094517 (DE-576)213783134 Bayes-Netz gnd (DE-627) |
format_facet |
Elektronische Bücher Bücher Elektronische Ressource Hochschulschriften |
standort_txtP_mv |
9300 |
signature |
--%%-- |
signature_str_mv |
--%%-- |
isfreeaccess_txt |
true |
normlinkwithrole_str_mv |
(DE-588)1074045890@@aut@@ (DE-588)4113937-9@@655@@ (DE-588)4123823-0@@689@@ (DE-588)4137042-9@@689@@ (DE-588)4567228-3@@689@@ |
ctrlnum |
(DE-627)782264476 (DE-599)DNB1048476014 (OCoLC)875594439 |
title_full |
Applications of Bayesian networks in natural hazard assessments Kristin Vogel. Betreuer: Frank Scherbaum |
author_sort |
Vogel, Kristin |
isOA_bool |
true |
genre |
Hochschulschrift (DE-588)4113937-9 (DE-627)105825778 (DE-576)209480580 gnd-content |
publishDateSort |
2014 |
selectkey |
20:x 21:z 22:x 23:x 30:z 40:x 60:z 63:x 70:z 105:z 110:x 132:x 151:z 161:x 285:z 293:x 370:x |
foreign_ids_str_mv |
(DE-101)1048476014 |
format_se |
Elektronische Bücher |
countryofpublication_str_mv |
XA-DE-BB |
author-letter |
Vogel, Kristin |
dewey-full |
550 |
author2-role |
betreuer |
title_sort |
applications of bayesian networks in natural hazard assessments |
title_auth |
Applications of Bayesian networks in natural hazard assessments |
abstract |
Even though quite different in occurrence and consequences, from a modeling perspective many natural hazards share similar properties and challenges. Their complex nature as well as lacking knowledge about their driving forces and potential effects make their analysis demanding: uncertainty about the modeling framework, inaccurate or incomplete event observations and the intrinsic randomness of the natural phenomenon add up to different interacting layers of uncertainty, which require a careful handling. Nevertheless deterministic approaches are still widely used in natural hazard assessments, holding the risk of underestimating the hazard with disastrous effects. The all-round probabilistic framework of Bayesian networks constitutes an attractive alternative. In contrast to deterministic proceedings, it treats response variables as well as explanatory variables as random variables making no difference between input and output variables. Using a graphical representation Bayesian networks encode the dependency relations between the variables in a directed acyclic graph: variables are represented as nodes and (in-)dependencies between variables as (missing) edges between the nodes. The joint distribution of all variables can thus be described by decomposing it, according to the depicted independences, into a product of local conditional probability distributions, which are defined by the parameters of the Bayesian network. In the framework of this thesis the Bayesian network approach is applied to different natural hazard domains (i.e. seismic hazard, flood damage and landslide assessments). Learning the network structure and parameters from data, Bayesian networks reveal relevant dependency relations between the included variables and help to gain knowledge about the underlying processes. The problem of Bayesian network learning is cast in a Bayesian framework, considering the network structure and parameters as random variables itself and searching for the most likely combination of both, which corresponds to the maximum a posteriori (MAP score) of their joint distribution given the observed data. Although well studied in theory the learning of Bayesian networks based on real-world data is usually not straight forward and requires an adoption of existing algorithms. Typically arising problems are the handling of continuous variables, incomplete observations and the interaction of both. Working with continuous distributions requires assumptions about the allowed families of distributions. To "let the data speak" and avoid wrong assumptions, continuous variables are instead discretized here, thus allowing for a completely data-driven and distribution-free learning. An extension of the MAP score, considering the discretization as random variable as well, is developed for an automatic multivariate discretization, that takes interactions between the variables into account. The discretization process is nested into the network learning and requires several iterations. Having to face incomplete observations on top, this may pose a computational burden. Iterative proceedings for missing value estimation become quickly infeasible. A more efficient albeit approximate method is used instead, estimating the missing values based only on the observations of variables directly interacting with the missing variable. Moreover natural hazard assessments often have a primary interest in a certain target variable. The discretization learned for this variable does not always have the required resolution for a good prediction performance. Finer resolutions for (conditional) continuous distributions are achieved with continuous approximations subsequent to the Bayesian network learning, using kernel density estimations or mixtures of truncated exponential functions. All our proceedings are completely data-driven. We thus avoid assumptions that require expert knowledge and instead provide domain independent solutions, that are applicable not only in other natural hazard assessments, but in a variety of domains struggling with uncertainties. |
abstractGer |
Even though quite different in occurrence and consequences, from a modeling perspective many natural hazards share similar properties and challenges. Their complex nature as well as lacking knowledge about their driving forces and potential effects make their analysis demanding: uncertainty about the modeling framework, inaccurate or incomplete event observations and the intrinsic randomness of the natural phenomenon add up to different interacting layers of uncertainty, which require a careful handling. Nevertheless deterministic approaches are still widely used in natural hazard assessments, holding the risk of underestimating the hazard with disastrous effects. The all-round probabilistic framework of Bayesian networks constitutes an attractive alternative. In contrast to deterministic proceedings, it treats response variables as well as explanatory variables as random variables making no difference between input and output variables. Using a graphical representation Bayesian networks encode the dependency relations between the variables in a directed acyclic graph: variables are represented as nodes and (in-)dependencies between variables as (missing) edges between the nodes. The joint distribution of all variables can thus be described by decomposing it, according to the depicted independences, into a product of local conditional probability distributions, which are defined by the parameters of the Bayesian network. In the framework of this thesis the Bayesian network approach is applied to different natural hazard domains (i.e. seismic hazard, flood damage and landslide assessments). Learning the network structure and parameters from data, Bayesian networks reveal relevant dependency relations between the included variables and help to gain knowledge about the underlying processes. The problem of Bayesian network learning is cast in a Bayesian framework, considering the network structure and parameters as random variables itself and searching for the most likely combination of both, which corresponds to the maximum a posteriori (MAP score) of their joint distribution given the observed data. Although well studied in theory the learning of Bayesian networks based on real-world data is usually not straight forward and requires an adoption of existing algorithms. Typically arising problems are the handling of continuous variables, incomplete observations and the interaction of both. Working with continuous distributions requires assumptions about the allowed families of distributions. To "let the data speak" and avoid wrong assumptions, continuous variables are instead discretized here, thus allowing for a completely data-driven and distribution-free learning. An extension of the MAP score, considering the discretization as random variable as well, is developed for an automatic multivariate discretization, that takes interactions between the variables into account. The discretization process is nested into the network learning and requires several iterations. Having to face incomplete observations on top, this may pose a computational burden. Iterative proceedings for missing value estimation become quickly infeasible. A more efficient albeit approximate method is used instead, estimating the missing values based only on the observations of variables directly interacting with the missing variable. Moreover natural hazard assessments often have a primary interest in a certain target variable. The discretization learned for this variable does not always have the required resolution for a good prediction performance. Finer resolutions for (conditional) continuous distributions are achieved with continuous approximations subsequent to the Bayesian network learning, using kernel density estimations or mixtures of truncated exponential functions. All our proceedings are completely data-driven. We thus avoid assumptions that require expert knowledge and instead provide domain independent solutions, that are applicable not only in other natural hazard assessments, but in a variety of domains struggling with uncertainties. |
abstract_unstemmed |
Even though quite different in occurrence and consequences, from a modeling perspective many natural hazards share similar properties and challenges. Their complex nature as well as lacking knowledge about their driving forces and potential effects make their analysis demanding: uncertainty about the modeling framework, inaccurate or incomplete event observations and the intrinsic randomness of the natural phenomenon add up to different interacting layers of uncertainty, which require a careful handling. Nevertheless deterministic approaches are still widely used in natural hazard assessments, holding the risk of underestimating the hazard with disastrous effects. The all-round probabilistic framework of Bayesian networks constitutes an attractive alternative. In contrast to deterministic proceedings, it treats response variables as well as explanatory variables as random variables making no difference between input and output variables. Using a graphical representation Bayesian networks encode the dependency relations between the variables in a directed acyclic graph: variables are represented as nodes and (in-)dependencies between variables as (missing) edges between the nodes. The joint distribution of all variables can thus be described by decomposing it, according to the depicted independences, into a product of local conditional probability distributions, which are defined by the parameters of the Bayesian network. In the framework of this thesis the Bayesian network approach is applied to different natural hazard domains (i.e. seismic hazard, flood damage and landslide assessments). Learning the network structure and parameters from data, Bayesian networks reveal relevant dependency relations between the included variables and help to gain knowledge about the underlying processes. The problem of Bayesian network learning is cast in a Bayesian framework, considering the network structure and parameters as random variables itself and searching for the most likely combination of both, which corresponds to the maximum a posteriori (MAP score) of their joint distribution given the observed data. Although well studied in theory the learning of Bayesian networks based on real-world data is usually not straight forward and requires an adoption of existing algorithms. Typically arising problems are the handling of continuous variables, incomplete observations and the interaction of both. Working with continuous distributions requires assumptions about the allowed families of distributions. To "let the data speak" and avoid wrong assumptions, continuous variables are instead discretized here, thus allowing for a completely data-driven and distribution-free learning. An extension of the MAP score, considering the discretization as random variable as well, is developed for an automatic multivariate discretization, that takes interactions between the variables into account. The discretization process is nested into the network learning and requires several iterations. Having to face incomplete observations on top, this may pose a computational burden. Iterative proceedings for missing value estimation become quickly infeasible. A more efficient albeit approximate method is used instead, estimating the missing values based only on the observations of variables directly interacting with the missing variable. Moreover natural hazard assessments often have a primary interest in a certain target variable. The discretization learned for this variable does not always have the required resolution for a good prediction performance. Finer resolutions for (conditional) continuous distributions are achieved with continuous approximations subsequent to the Bayesian network learning, using kernel density estimations or mixtures of truncated exponential functions. All our proceedings are completely data-driven. We thus avoid assumptions that require expert knowledge and instead provide domain independent solutions, that are applicable not only in other natural hazard assessments, but in a variety of domains struggling with uncertainties. |
url |
http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 http://d-nb.info/1048476014/34 http://opus.kobv.de/ubp/volltexte/2014/6977/ |
author2 |
Scherbaum, Frank |
author2Str |
Scherbaum, Frank |
ppnlink |
830342885 |
mediatype_str_mv |
c |
isOA_txt |
true |
hochschulschrift_bool |
true |
author2_role |
oth |
author_variant |
k v kv |
hierarchy_sort_str |
2014 |
bklnumber |
43.48 31.70 |
allfields |
14,O04 dnb 1048476014 DE-101 urn:nbn:de:kobv:517-opus-69777 urn (DE-627)782264476 (DE-599)DNB1048476014 (OCoLC)875594439 DE-627 ger DE-627 rakwb eng XA-DE-BB 550 DNB 43.48 bkl 31.70 bkl Vogel, Kristin verfasserin (DE-588)1074045890 (DE-627)830216421 (DE-576)435559109 aut Applications of Bayesian networks in natural hazard assessments Kristin Vogel. Betreuer: Frank Scherbaum Parallelsacht. Anwendungen von Bayes'schen Netzen bei der Einschätzung von Naturgefahren 2014 Online-Ressource (PDF-Datei: 11441 KB, X, 84 Bl.) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Potsdam, Univ., Kumulative Diss., 2014 Even though quite different in occurrence and consequences, from a modeling perspective many natural hazards share similar properties and challenges. Their complex nature as well as lacking knowledge about their driving forces and potential effects make their analysis demanding: uncertainty about the modeling framework, inaccurate or incomplete event observations and the intrinsic randomness of the natural phenomenon add up to different interacting layers of uncertainty, which require a careful handling. Nevertheless deterministic approaches are still widely used in natural hazard assessments, holding the risk of underestimating the hazard with disastrous effects. The all-round probabilistic framework of Bayesian networks constitutes an attractive alternative. In contrast to deterministic proceedings, it treats response variables as well as explanatory variables as random variables making no difference between input and output variables. Using a graphical representation Bayesian networks encode the dependency relations between the variables in a directed acyclic graph: variables are represented as nodes and (in-)dependencies between variables as (missing) edges between the nodes. The joint distribution of all variables can thus be described by decomposing it, according to the depicted independences, into a product of local conditional probability distributions, which are defined by the parameters of the Bayesian network. In the framework of this thesis the Bayesian network approach is applied to different natural hazard domains (i.e. seismic hazard, flood damage and landslide assessments). Learning the network structure and parameters from data, Bayesian networks reveal relevant dependency relations between the included variables and help to gain knowledge about the underlying processes. The problem of Bayesian network learning is cast in a Bayesian framework, considering the network structure and parameters as random variables itself and searching for the most likely combination of both, which corresponds to the maximum a posteriori (MAP score) of their joint distribution given the observed data. Although well studied in theory the learning of Bayesian networks based on real-world data is usually not straight forward and requires an adoption of existing algorithms. Typically arising problems are the handling of continuous variables, incomplete observations and the interaction of both. Working with continuous distributions requires assumptions about the allowed families of distributions. To "let the data speak" and avoid wrong assumptions, continuous variables are instead discretized here, thus allowing for a completely data-driven and distribution-free learning. An extension of the MAP score, considering the discretization as random variable as well, is developed for an automatic multivariate discretization, that takes interactions between the variables into account. The discretization process is nested into the network learning and requires several iterations. Having to face incomplete observations on top, this may pose a computational burden. Iterative proceedings for missing value estimation become quickly infeasible. A more efficient albeit approximate method is used instead, estimating the missing values based only on the observations of variables directly interacting with the missing variable. Moreover natural hazard assessments often have a primary interest in a certain target variable. The discretization learned for this variable does not always have the required resolution for a good prediction performance. Finer resolutions for (conditional) continuous distributions are achieved with continuous approximations subsequent to the Bayesian network learning, using kernel density estimations or mixtures of truncated exponential functions. All our proceedings are completely data-driven. We thus avoid assumptions that require expert knowledge and instead provide domain independent solutions, that are applicable not only in other natural hazard assessments, but in a variety of domains struggling with uncertainties. Langzeitarchivierung gewährleistet pdager Hochschulschrift (DE-588)4113937-9 (DE-627)105825778 (DE-576)209480580 gnd-content s (DE-588)4123823-0 (DE-627)105752304 (DE-576)209562978 Naturgefahr gnd s (DE-588)4137042-9 (DE-627)10427932X (DE-576)209674318 Risikoanalyse gnd s (DE-588)4567228-3 (DE-627)306094517 (DE-576)213783134 Bayes-Netz gnd (DE-627) Scherbaum, Frank betreuer oth Potsdam uvp Druckausg. Vogel, Kristin Applications of Bayesian networks in natural hazard assessments Potsdam, 2013 x, 84 Blätter (DE-627)830342885 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 2014-04-02 Resolving-System Volltext http://d-nb.info/1048476014/34 2014-04-02 Langzeitarchivierung Nationalbibliothek Volltext http://opus.kobv.de/ubp/volltexte/2014/6977/ 2014-04-02 Verlag kostenfrei Volltext GBV-ODiss GBV_ILN_20 ISIL_DE-84 SYSFLAG_1 GBV_KXP SSG-OPC-GGO GBV_ILN_21 ISIL_DE-46 GBV_ILN_22 ISIL_DE-18 GBV_ILN_23 ISIL_DE-830 GBV_ILN_30 ISIL_DE-104 GBV_ILN_40 ISIL_DE-7 GBV_ILN_60 ISIL_DE-705 GBV_ILN_63 ISIL_DE-Wim2 GBV_ILN_70 ISIL_DE-89 GBV_ILN_105 ISIL_DE-841 GBV_ILN_110 ISIL_DE-Luen4 GBV_ILN_132 ISIL_DE-959 GBV_ILN_151 ISIL_DE-546 GBV_ILN_161 ISIL_DE-960 GBV_ILN_285 ISIL_DE-517 GBV_ILN_293 ISIL_DE-960-3 GBV_ILN_370 ISIL_DE-1373 43.48 Regionale Umweltprobleme (DE-627)181569981 31.70 Wahrscheinlichkeitsrechnung (DE-627)106408070 BO 20 01 0084 1475518501 x 29-05-14 21 01 0046 1475536720 z 29-05-14 22 01 0018 1475554664 SUBolrd xu 29-05-14 23 01 0830 1475568061 olr-d x 29-05-14 30 01 0104 1475578156 z 29-05-14 40 01 0007 1475590288 xsn 29-05-14 60 01 0705 1475608004 OLRD z 29-05-14 63 01 3401 1595845496 ORD x 21-01-16 70 01 0089 1475634854 zdo 29-05-14 105 01 0841 1596657936 z 21-01-16 110 01 3110 1475623658 x 29-05-14 132 01 0959 1498549047 OLR-DISS x 14-08-14 151 01 0546 3594257711 OLR-ODISS z 13-02-20 161 01 0960 1597648639 ORD x 28-01-16 285 01 0517 1554521785 00 9300 --%%-- s --%%-- z 21-07-15 293 01 3293 1598162004 ORD xf 28-01-16 370 01 4370 1475630204 x 29-05-14 20 01 0084 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 21 01 0046 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 22 01 0018 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 23 01 0830 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 30 01 0104 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 40 01 0007 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 60 01 0705 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 63 01 3401 E-Book http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 LF 70 01 0089 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 105 01 0841 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 110 01 3110 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 132 01 0959 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 151 01 0546 Volltext http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 161 01 0960 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 285 01 0517 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 293 01 3293 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 370 01 4370 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 60 01 0705 10 ho 285 00 DE-517 00 TF 04999 285 00 DE-517 00 AR 14120 285 00 DE-517 00 SK 830 285 00 DE-517 00 UT 1800 20 01 0084 OLRD 110 01 3110 OLRD 370 01 4370 OLRD 22 01 0018 SUBolrd 23 01 0830 olr-d 60 01 0705 OLRD 63 01 3401 ORD 132 01 0959 OLR-DISS 151 01 0546 OLR-ODISS 161 01 0960 ORD 293 01 3293 ORD 23 01 0830 2014-05-29:01:06:56 |
spelling |
14,O04 dnb 1048476014 DE-101 urn:nbn:de:kobv:517-opus-69777 urn (DE-627)782264476 (DE-599)DNB1048476014 (OCoLC)875594439 DE-627 ger DE-627 rakwb eng XA-DE-BB 550 DNB 43.48 bkl 31.70 bkl Vogel, Kristin verfasserin (DE-588)1074045890 (DE-627)830216421 (DE-576)435559109 aut Applications of Bayesian networks in natural hazard assessments Kristin Vogel. Betreuer: Frank Scherbaum Parallelsacht. Anwendungen von Bayes'schen Netzen bei der Einschätzung von Naturgefahren 2014 Online-Ressource (PDF-Datei: 11441 KB, X, 84 Bl.) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Potsdam, Univ., Kumulative Diss., 2014 Even though quite different in occurrence and consequences, from a modeling perspective many natural hazards share similar properties and challenges. Their complex nature as well as lacking knowledge about their driving forces and potential effects make their analysis demanding: uncertainty about the modeling framework, inaccurate or incomplete event observations and the intrinsic randomness of the natural phenomenon add up to different interacting layers of uncertainty, which require a careful handling. Nevertheless deterministic approaches are still widely used in natural hazard assessments, holding the risk of underestimating the hazard with disastrous effects. The all-round probabilistic framework of Bayesian networks constitutes an attractive alternative. In contrast to deterministic proceedings, it treats response variables as well as explanatory variables as random variables making no difference between input and output variables. Using a graphical representation Bayesian networks encode the dependency relations between the variables in a directed acyclic graph: variables are represented as nodes and (in-)dependencies between variables as (missing) edges between the nodes. The joint distribution of all variables can thus be described by decomposing it, according to the depicted independences, into a product of local conditional probability distributions, which are defined by the parameters of the Bayesian network. In the framework of this thesis the Bayesian network approach is applied to different natural hazard domains (i.e. seismic hazard, flood damage and landslide assessments). Learning the network structure and parameters from data, Bayesian networks reveal relevant dependency relations between the included variables and help to gain knowledge about the underlying processes. The problem of Bayesian network learning is cast in a Bayesian framework, considering the network structure and parameters as random variables itself and searching for the most likely combination of both, which corresponds to the maximum a posteriori (MAP score) of their joint distribution given the observed data. Although well studied in theory the learning of Bayesian networks based on real-world data is usually not straight forward and requires an adoption of existing algorithms. Typically arising problems are the handling of continuous variables, incomplete observations and the interaction of both. Working with continuous distributions requires assumptions about the allowed families of distributions. To "let the data speak" and avoid wrong assumptions, continuous variables are instead discretized here, thus allowing for a completely data-driven and distribution-free learning. An extension of the MAP score, considering the discretization as random variable as well, is developed for an automatic multivariate discretization, that takes interactions between the variables into account. The discretization process is nested into the network learning and requires several iterations. Having to face incomplete observations on top, this may pose a computational burden. Iterative proceedings for missing value estimation become quickly infeasible. A more efficient albeit approximate method is used instead, estimating the missing values based only on the observations of variables directly interacting with the missing variable. Moreover natural hazard assessments often have a primary interest in a certain target variable. The discretization learned for this variable does not always have the required resolution for a good prediction performance. Finer resolutions for (conditional) continuous distributions are achieved with continuous approximations subsequent to the Bayesian network learning, using kernel density estimations or mixtures of truncated exponential functions. All our proceedings are completely data-driven. We thus avoid assumptions that require expert knowledge and instead provide domain independent solutions, that are applicable not only in other natural hazard assessments, but in a variety of domains struggling with uncertainties. Langzeitarchivierung gewährleistet pdager Hochschulschrift (DE-588)4113937-9 (DE-627)105825778 (DE-576)209480580 gnd-content s (DE-588)4123823-0 (DE-627)105752304 (DE-576)209562978 Naturgefahr gnd s (DE-588)4137042-9 (DE-627)10427932X (DE-576)209674318 Risikoanalyse gnd s (DE-588)4567228-3 (DE-627)306094517 (DE-576)213783134 Bayes-Netz gnd (DE-627) Scherbaum, Frank betreuer oth Potsdam uvp Druckausg. Vogel, Kristin Applications of Bayesian networks in natural hazard assessments Potsdam, 2013 x, 84 Blätter (DE-627)830342885 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 2014-04-02 Resolving-System Volltext http://d-nb.info/1048476014/34 2014-04-02 Langzeitarchivierung Nationalbibliothek Volltext http://opus.kobv.de/ubp/volltexte/2014/6977/ 2014-04-02 Verlag kostenfrei Volltext GBV-ODiss GBV_ILN_20 ISIL_DE-84 SYSFLAG_1 GBV_KXP SSG-OPC-GGO GBV_ILN_21 ISIL_DE-46 GBV_ILN_22 ISIL_DE-18 GBV_ILN_23 ISIL_DE-830 GBV_ILN_30 ISIL_DE-104 GBV_ILN_40 ISIL_DE-7 GBV_ILN_60 ISIL_DE-705 GBV_ILN_63 ISIL_DE-Wim2 GBV_ILN_70 ISIL_DE-89 GBV_ILN_105 ISIL_DE-841 GBV_ILN_110 ISIL_DE-Luen4 GBV_ILN_132 ISIL_DE-959 GBV_ILN_151 ISIL_DE-546 GBV_ILN_161 ISIL_DE-960 GBV_ILN_285 ISIL_DE-517 GBV_ILN_293 ISIL_DE-960-3 GBV_ILN_370 ISIL_DE-1373 43.48 Regionale Umweltprobleme (DE-627)181569981 31.70 Wahrscheinlichkeitsrechnung (DE-627)106408070 BO 20 01 0084 1475518501 x 29-05-14 21 01 0046 1475536720 z 29-05-14 22 01 0018 1475554664 SUBolrd xu 29-05-14 23 01 0830 1475568061 olr-d x 29-05-14 30 01 0104 1475578156 z 29-05-14 40 01 0007 1475590288 xsn 29-05-14 60 01 0705 1475608004 OLRD z 29-05-14 63 01 3401 1595845496 ORD x 21-01-16 70 01 0089 1475634854 zdo 29-05-14 105 01 0841 1596657936 z 21-01-16 110 01 3110 1475623658 x 29-05-14 132 01 0959 1498549047 OLR-DISS x 14-08-14 151 01 0546 3594257711 OLR-ODISS z 13-02-20 161 01 0960 1597648639 ORD x 28-01-16 285 01 0517 1554521785 00 9300 --%%-- s --%%-- z 21-07-15 293 01 3293 1598162004 ORD xf 28-01-16 370 01 4370 1475630204 x 29-05-14 20 01 0084 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 21 01 0046 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 22 01 0018 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 23 01 0830 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 30 01 0104 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 40 01 0007 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 60 01 0705 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 63 01 3401 E-Book http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 LF 70 01 0089 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 105 01 0841 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 110 01 3110 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 132 01 0959 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 151 01 0546 Volltext http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 161 01 0960 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 285 01 0517 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 293 01 3293 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 370 01 4370 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 60 01 0705 10 ho 285 00 DE-517 00 TF 04999 285 00 DE-517 00 AR 14120 285 00 DE-517 00 SK 830 285 00 DE-517 00 UT 1800 20 01 0084 OLRD 110 01 3110 OLRD 370 01 4370 OLRD 22 01 0018 SUBolrd 23 01 0830 olr-d 60 01 0705 OLRD 63 01 3401 ORD 132 01 0959 OLR-DISS 151 01 0546 OLR-ODISS 161 01 0960 ORD 293 01 3293 ORD 23 01 0830 2014-05-29:01:06:56 |
allfields_unstemmed |
14,O04 dnb 1048476014 DE-101 urn:nbn:de:kobv:517-opus-69777 urn (DE-627)782264476 (DE-599)DNB1048476014 (OCoLC)875594439 DE-627 ger DE-627 rakwb eng XA-DE-BB 550 DNB 43.48 bkl 31.70 bkl Vogel, Kristin verfasserin (DE-588)1074045890 (DE-627)830216421 (DE-576)435559109 aut Applications of Bayesian networks in natural hazard assessments Kristin Vogel. Betreuer: Frank Scherbaum Parallelsacht. Anwendungen von Bayes'schen Netzen bei der Einschätzung von Naturgefahren 2014 Online-Ressource (PDF-Datei: 11441 KB, X, 84 Bl.) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Potsdam, Univ., Kumulative Diss., 2014 Even though quite different in occurrence and consequences, from a modeling perspective many natural hazards share similar properties and challenges. Their complex nature as well as lacking knowledge about their driving forces and potential effects make their analysis demanding: uncertainty about the modeling framework, inaccurate or incomplete event observations and the intrinsic randomness of the natural phenomenon add up to different interacting layers of uncertainty, which require a careful handling. Nevertheless deterministic approaches are still widely used in natural hazard assessments, holding the risk of underestimating the hazard with disastrous effects. The all-round probabilistic framework of Bayesian networks constitutes an attractive alternative. In contrast to deterministic proceedings, it treats response variables as well as explanatory variables as random variables making no difference between input and output variables. Using a graphical representation Bayesian networks encode the dependency relations between the variables in a directed acyclic graph: variables are represented as nodes and (in-)dependencies between variables as (missing) edges between the nodes. The joint distribution of all variables can thus be described by decomposing it, according to the depicted independences, into a product of local conditional probability distributions, which are defined by the parameters of the Bayesian network. In the framework of this thesis the Bayesian network approach is applied to different natural hazard domains (i.e. seismic hazard, flood damage and landslide assessments). Learning the network structure and parameters from data, Bayesian networks reveal relevant dependency relations between the included variables and help to gain knowledge about the underlying processes. The problem of Bayesian network learning is cast in a Bayesian framework, considering the network structure and parameters as random variables itself and searching for the most likely combination of both, which corresponds to the maximum a posteriori (MAP score) of their joint distribution given the observed data. Although well studied in theory the learning of Bayesian networks based on real-world data is usually not straight forward and requires an adoption of existing algorithms. Typically arising problems are the handling of continuous variables, incomplete observations and the interaction of both. Working with continuous distributions requires assumptions about the allowed families of distributions. To "let the data speak" and avoid wrong assumptions, continuous variables are instead discretized here, thus allowing for a completely data-driven and distribution-free learning. An extension of the MAP score, considering the discretization as random variable as well, is developed for an automatic multivariate discretization, that takes interactions between the variables into account. The discretization process is nested into the network learning and requires several iterations. Having to face incomplete observations on top, this may pose a computational burden. Iterative proceedings for missing value estimation become quickly infeasible. A more efficient albeit approximate method is used instead, estimating the missing values based only on the observations of variables directly interacting with the missing variable. Moreover natural hazard assessments often have a primary interest in a certain target variable. The discretization learned for this variable does not always have the required resolution for a good prediction performance. Finer resolutions for (conditional) continuous distributions are achieved with continuous approximations subsequent to the Bayesian network learning, using kernel density estimations or mixtures of truncated exponential functions. All our proceedings are completely data-driven. We thus avoid assumptions that require expert knowledge and instead provide domain independent solutions, that are applicable not only in other natural hazard assessments, but in a variety of domains struggling with uncertainties. Langzeitarchivierung gewährleistet pdager Hochschulschrift (DE-588)4113937-9 (DE-627)105825778 (DE-576)209480580 gnd-content s (DE-588)4123823-0 (DE-627)105752304 (DE-576)209562978 Naturgefahr gnd s (DE-588)4137042-9 (DE-627)10427932X (DE-576)209674318 Risikoanalyse gnd s (DE-588)4567228-3 (DE-627)306094517 (DE-576)213783134 Bayes-Netz gnd (DE-627) Scherbaum, Frank betreuer oth Potsdam uvp Druckausg. Vogel, Kristin Applications of Bayesian networks in natural hazard assessments Potsdam, 2013 x, 84 Blätter (DE-627)830342885 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 2014-04-02 Resolving-System Volltext http://d-nb.info/1048476014/34 2014-04-02 Langzeitarchivierung Nationalbibliothek Volltext http://opus.kobv.de/ubp/volltexte/2014/6977/ 2014-04-02 Verlag kostenfrei Volltext GBV-ODiss GBV_ILN_20 ISIL_DE-84 SYSFLAG_1 GBV_KXP SSG-OPC-GGO GBV_ILN_21 ISIL_DE-46 GBV_ILN_22 ISIL_DE-18 GBV_ILN_23 ISIL_DE-830 GBV_ILN_30 ISIL_DE-104 GBV_ILN_40 ISIL_DE-7 GBV_ILN_60 ISIL_DE-705 GBV_ILN_63 ISIL_DE-Wim2 GBV_ILN_70 ISIL_DE-89 GBV_ILN_105 ISIL_DE-841 GBV_ILN_110 ISIL_DE-Luen4 GBV_ILN_132 ISIL_DE-959 GBV_ILN_151 ISIL_DE-546 GBV_ILN_161 ISIL_DE-960 GBV_ILN_285 ISIL_DE-517 GBV_ILN_293 ISIL_DE-960-3 GBV_ILN_370 ISIL_DE-1373 43.48 Regionale Umweltprobleme (DE-627)181569981 31.70 Wahrscheinlichkeitsrechnung (DE-627)106408070 BO 20 01 0084 1475518501 x 29-05-14 21 01 0046 1475536720 z 29-05-14 22 01 0018 1475554664 SUBolrd xu 29-05-14 23 01 0830 1475568061 olr-d x 29-05-14 30 01 0104 1475578156 z 29-05-14 40 01 0007 1475590288 xsn 29-05-14 60 01 0705 1475608004 OLRD z 29-05-14 63 01 3401 1595845496 ORD x 21-01-16 70 01 0089 1475634854 zdo 29-05-14 105 01 0841 1596657936 z 21-01-16 110 01 3110 1475623658 x 29-05-14 132 01 0959 1498549047 OLR-DISS x 14-08-14 151 01 0546 3594257711 OLR-ODISS z 13-02-20 161 01 0960 1597648639 ORD x 28-01-16 285 01 0517 1554521785 00 9300 --%%-- s --%%-- z 21-07-15 293 01 3293 1598162004 ORD xf 28-01-16 370 01 4370 1475630204 x 29-05-14 20 01 0084 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 21 01 0046 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 22 01 0018 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 23 01 0830 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 30 01 0104 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 40 01 0007 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 60 01 0705 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 63 01 3401 E-Book http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 LF 70 01 0089 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 105 01 0841 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 110 01 3110 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 132 01 0959 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 151 01 0546 Volltext http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 161 01 0960 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 285 01 0517 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 293 01 3293 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 370 01 4370 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 60 01 0705 10 ho 285 00 DE-517 00 TF 04999 285 00 DE-517 00 AR 14120 285 00 DE-517 00 SK 830 285 00 DE-517 00 UT 1800 20 01 0084 OLRD 110 01 3110 OLRD 370 01 4370 OLRD 22 01 0018 SUBolrd 23 01 0830 olr-d 60 01 0705 OLRD 63 01 3401 ORD 132 01 0959 OLR-DISS 151 01 0546 OLR-ODISS 161 01 0960 ORD 293 01 3293 ORD 23 01 0830 2014-05-29:01:06:56 |
allfieldsGer |
14,O04 dnb 1048476014 DE-101 urn:nbn:de:kobv:517-opus-69777 urn (DE-627)782264476 (DE-599)DNB1048476014 (OCoLC)875594439 DE-627 ger DE-627 rakwb eng XA-DE-BB 550 DNB 43.48 bkl 31.70 bkl Vogel, Kristin verfasserin (DE-588)1074045890 (DE-627)830216421 (DE-576)435559109 aut Applications of Bayesian networks in natural hazard assessments Kristin Vogel. Betreuer: Frank Scherbaum Parallelsacht. Anwendungen von Bayes'schen Netzen bei der Einschätzung von Naturgefahren 2014 Online-Ressource (PDF-Datei: 11441 KB, X, 84 Bl.) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Potsdam, Univ., Kumulative Diss., 2014 Even though quite different in occurrence and consequences, from a modeling perspective many natural hazards share similar properties and challenges. Their complex nature as well as lacking knowledge about their driving forces and potential effects make their analysis demanding: uncertainty about the modeling framework, inaccurate or incomplete event observations and the intrinsic randomness of the natural phenomenon add up to different interacting layers of uncertainty, which require a careful handling. Nevertheless deterministic approaches are still widely used in natural hazard assessments, holding the risk of underestimating the hazard with disastrous effects. The all-round probabilistic framework of Bayesian networks constitutes an attractive alternative. In contrast to deterministic proceedings, it treats response variables as well as explanatory variables as random variables making no difference between input and output variables. Using a graphical representation Bayesian networks encode the dependency relations between the variables in a directed acyclic graph: variables are represented as nodes and (in-)dependencies between variables as (missing) edges between the nodes. The joint distribution of all variables can thus be described by decomposing it, according to the depicted independences, into a product of local conditional probability distributions, which are defined by the parameters of the Bayesian network. In the framework of this thesis the Bayesian network approach is applied to different natural hazard domains (i.e. seismic hazard, flood damage and landslide assessments). Learning the network structure and parameters from data, Bayesian networks reveal relevant dependency relations between the included variables and help to gain knowledge about the underlying processes. The problem of Bayesian network learning is cast in a Bayesian framework, considering the network structure and parameters as random variables itself and searching for the most likely combination of both, which corresponds to the maximum a posteriori (MAP score) of their joint distribution given the observed data. Although well studied in theory the learning of Bayesian networks based on real-world data is usually not straight forward and requires an adoption of existing algorithms. Typically arising problems are the handling of continuous variables, incomplete observations and the interaction of both. Working with continuous distributions requires assumptions about the allowed families of distributions. To "let the data speak" and avoid wrong assumptions, continuous variables are instead discretized here, thus allowing for a completely data-driven and distribution-free learning. An extension of the MAP score, considering the discretization as random variable as well, is developed for an automatic multivariate discretization, that takes interactions between the variables into account. The discretization process is nested into the network learning and requires several iterations. Having to face incomplete observations on top, this may pose a computational burden. Iterative proceedings for missing value estimation become quickly infeasible. A more efficient albeit approximate method is used instead, estimating the missing values based only on the observations of variables directly interacting with the missing variable. Moreover natural hazard assessments often have a primary interest in a certain target variable. The discretization learned for this variable does not always have the required resolution for a good prediction performance. Finer resolutions for (conditional) continuous distributions are achieved with continuous approximations subsequent to the Bayesian network learning, using kernel density estimations or mixtures of truncated exponential functions. All our proceedings are completely data-driven. We thus avoid assumptions that require expert knowledge and instead provide domain independent solutions, that are applicable not only in other natural hazard assessments, but in a variety of domains struggling with uncertainties. Langzeitarchivierung gewährleistet pdager Hochschulschrift (DE-588)4113937-9 (DE-627)105825778 (DE-576)209480580 gnd-content s (DE-588)4123823-0 (DE-627)105752304 (DE-576)209562978 Naturgefahr gnd s (DE-588)4137042-9 (DE-627)10427932X (DE-576)209674318 Risikoanalyse gnd s (DE-588)4567228-3 (DE-627)306094517 (DE-576)213783134 Bayes-Netz gnd (DE-627) Scherbaum, Frank betreuer oth Potsdam uvp Druckausg. Vogel, Kristin Applications of Bayesian networks in natural hazard assessments Potsdam, 2013 x, 84 Blätter (DE-627)830342885 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 2014-04-02 Resolving-System Volltext http://d-nb.info/1048476014/34 2014-04-02 Langzeitarchivierung Nationalbibliothek Volltext http://opus.kobv.de/ubp/volltexte/2014/6977/ 2014-04-02 Verlag kostenfrei Volltext GBV-ODiss GBV_ILN_20 ISIL_DE-84 SYSFLAG_1 GBV_KXP SSG-OPC-GGO GBV_ILN_21 ISIL_DE-46 GBV_ILN_22 ISIL_DE-18 GBV_ILN_23 ISIL_DE-830 GBV_ILN_30 ISIL_DE-104 GBV_ILN_40 ISIL_DE-7 GBV_ILN_60 ISIL_DE-705 GBV_ILN_63 ISIL_DE-Wim2 GBV_ILN_70 ISIL_DE-89 GBV_ILN_105 ISIL_DE-841 GBV_ILN_110 ISIL_DE-Luen4 GBV_ILN_132 ISIL_DE-959 GBV_ILN_151 ISIL_DE-546 GBV_ILN_161 ISIL_DE-960 GBV_ILN_285 ISIL_DE-517 GBV_ILN_293 ISIL_DE-960-3 GBV_ILN_370 ISIL_DE-1373 43.48 Regionale Umweltprobleme (DE-627)181569981 31.70 Wahrscheinlichkeitsrechnung (DE-627)106408070 BO 20 01 0084 1475518501 x 29-05-14 21 01 0046 1475536720 z 29-05-14 22 01 0018 1475554664 SUBolrd xu 29-05-14 23 01 0830 1475568061 olr-d x 29-05-14 30 01 0104 1475578156 z 29-05-14 40 01 0007 1475590288 xsn 29-05-14 60 01 0705 1475608004 OLRD z 29-05-14 63 01 3401 1595845496 ORD x 21-01-16 70 01 0089 1475634854 zdo 29-05-14 105 01 0841 1596657936 z 21-01-16 110 01 3110 1475623658 x 29-05-14 132 01 0959 1498549047 OLR-DISS x 14-08-14 151 01 0546 3594257711 OLR-ODISS z 13-02-20 161 01 0960 1597648639 ORD x 28-01-16 285 01 0517 1554521785 00 9300 --%%-- s --%%-- z 21-07-15 293 01 3293 1598162004 ORD xf 28-01-16 370 01 4370 1475630204 x 29-05-14 20 01 0084 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 21 01 0046 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 22 01 0018 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 23 01 0830 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 30 01 0104 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 40 01 0007 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 60 01 0705 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 63 01 3401 E-Book http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 LF 70 01 0089 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 105 01 0841 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 110 01 3110 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 132 01 0959 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 151 01 0546 Volltext http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 161 01 0960 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 285 01 0517 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 293 01 3293 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 370 01 4370 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 60 01 0705 10 ho 285 00 DE-517 00 TF 04999 285 00 DE-517 00 AR 14120 285 00 DE-517 00 SK 830 285 00 DE-517 00 UT 1800 20 01 0084 OLRD 110 01 3110 OLRD 370 01 4370 OLRD 22 01 0018 SUBolrd 23 01 0830 olr-d 60 01 0705 OLRD 63 01 3401 ORD 132 01 0959 OLR-DISS 151 01 0546 OLR-ODISS 161 01 0960 ORD 293 01 3293 ORD 23 01 0830 2014-05-29:01:06:56 |
allfieldsSound |
14,O04 dnb 1048476014 DE-101 urn:nbn:de:kobv:517-opus-69777 urn (DE-627)782264476 (DE-599)DNB1048476014 (OCoLC)875594439 DE-627 ger DE-627 rakwb eng XA-DE-BB 550 DNB 43.48 bkl 31.70 bkl Vogel, Kristin verfasserin (DE-588)1074045890 (DE-627)830216421 (DE-576)435559109 aut Applications of Bayesian networks in natural hazard assessments Kristin Vogel. Betreuer: Frank Scherbaum Parallelsacht. Anwendungen von Bayes'schen Netzen bei der Einschätzung von Naturgefahren 2014 Online-Ressource (PDF-Datei: 11441 KB, X, 84 Bl.) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Potsdam, Univ., Kumulative Diss., 2014 Even though quite different in occurrence and consequences, from a modeling perspective many natural hazards share similar properties and challenges. Their complex nature as well as lacking knowledge about their driving forces and potential effects make their analysis demanding: uncertainty about the modeling framework, inaccurate or incomplete event observations and the intrinsic randomness of the natural phenomenon add up to different interacting layers of uncertainty, which require a careful handling. Nevertheless deterministic approaches are still widely used in natural hazard assessments, holding the risk of underestimating the hazard with disastrous effects. The all-round probabilistic framework of Bayesian networks constitutes an attractive alternative. In contrast to deterministic proceedings, it treats response variables as well as explanatory variables as random variables making no difference between input and output variables. Using a graphical representation Bayesian networks encode the dependency relations between the variables in a directed acyclic graph: variables are represented as nodes and (in-)dependencies between variables as (missing) edges between the nodes. The joint distribution of all variables can thus be described by decomposing it, according to the depicted independences, into a product of local conditional probability distributions, which are defined by the parameters of the Bayesian network. In the framework of this thesis the Bayesian network approach is applied to different natural hazard domains (i.e. seismic hazard, flood damage and landslide assessments). Learning the network structure and parameters from data, Bayesian networks reveal relevant dependency relations between the included variables and help to gain knowledge about the underlying processes. The problem of Bayesian network learning is cast in a Bayesian framework, considering the network structure and parameters as random variables itself and searching for the most likely combination of both, which corresponds to the maximum a posteriori (MAP score) of their joint distribution given the observed data. Although well studied in theory the learning of Bayesian networks based on real-world data is usually not straight forward and requires an adoption of existing algorithms. Typically arising problems are the handling of continuous variables, incomplete observations and the interaction of both. Working with continuous distributions requires assumptions about the allowed families of distributions. To "let the data speak" and avoid wrong assumptions, continuous variables are instead discretized here, thus allowing for a completely data-driven and distribution-free learning. An extension of the MAP score, considering the discretization as random variable as well, is developed for an automatic multivariate discretization, that takes interactions between the variables into account. The discretization process is nested into the network learning and requires several iterations. Having to face incomplete observations on top, this may pose a computational burden. Iterative proceedings for missing value estimation become quickly infeasible. A more efficient albeit approximate method is used instead, estimating the missing values based only on the observations of variables directly interacting with the missing variable. Moreover natural hazard assessments often have a primary interest in a certain target variable. The discretization learned for this variable does not always have the required resolution for a good prediction performance. Finer resolutions for (conditional) continuous distributions are achieved with continuous approximations subsequent to the Bayesian network learning, using kernel density estimations or mixtures of truncated exponential functions. All our proceedings are completely data-driven. We thus avoid assumptions that require expert knowledge and instead provide domain independent solutions, that are applicable not only in other natural hazard assessments, but in a variety of domains struggling with uncertainties. Langzeitarchivierung gewährleistet pdager Hochschulschrift (DE-588)4113937-9 (DE-627)105825778 (DE-576)209480580 gnd-content s (DE-588)4123823-0 (DE-627)105752304 (DE-576)209562978 Naturgefahr gnd s (DE-588)4137042-9 (DE-627)10427932X (DE-576)209674318 Risikoanalyse gnd s (DE-588)4567228-3 (DE-627)306094517 (DE-576)213783134 Bayes-Netz gnd (DE-627) Scherbaum, Frank betreuer oth Potsdam uvp Druckausg. Vogel, Kristin Applications of Bayesian networks in natural hazard assessments Potsdam, 2013 x, 84 Blätter (DE-627)830342885 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 2014-04-02 Resolving-System Volltext http://d-nb.info/1048476014/34 2014-04-02 Langzeitarchivierung Nationalbibliothek Volltext http://opus.kobv.de/ubp/volltexte/2014/6977/ 2014-04-02 Verlag kostenfrei Volltext GBV-ODiss GBV_ILN_20 ISIL_DE-84 SYSFLAG_1 GBV_KXP SSG-OPC-GGO GBV_ILN_21 ISIL_DE-46 GBV_ILN_22 ISIL_DE-18 GBV_ILN_23 ISIL_DE-830 GBV_ILN_30 ISIL_DE-104 GBV_ILN_40 ISIL_DE-7 GBV_ILN_60 ISIL_DE-705 GBV_ILN_63 ISIL_DE-Wim2 GBV_ILN_70 ISIL_DE-89 GBV_ILN_105 ISIL_DE-841 GBV_ILN_110 ISIL_DE-Luen4 GBV_ILN_132 ISIL_DE-959 GBV_ILN_151 ISIL_DE-546 GBV_ILN_161 ISIL_DE-960 GBV_ILN_285 ISIL_DE-517 GBV_ILN_293 ISIL_DE-960-3 GBV_ILN_370 ISIL_DE-1373 43.48 Regionale Umweltprobleme (DE-627)181569981 31.70 Wahrscheinlichkeitsrechnung (DE-627)106408070 BO 20 01 0084 1475518501 x 29-05-14 21 01 0046 1475536720 z 29-05-14 22 01 0018 1475554664 SUBolrd xu 29-05-14 23 01 0830 1475568061 olr-d x 29-05-14 30 01 0104 1475578156 z 29-05-14 40 01 0007 1475590288 xsn 29-05-14 60 01 0705 1475608004 OLRD z 29-05-14 63 01 3401 1595845496 ORD x 21-01-16 70 01 0089 1475634854 zdo 29-05-14 105 01 0841 1596657936 z 21-01-16 110 01 3110 1475623658 x 29-05-14 132 01 0959 1498549047 OLR-DISS x 14-08-14 151 01 0546 3594257711 OLR-ODISS z 13-02-20 161 01 0960 1597648639 ORD x 28-01-16 285 01 0517 1554521785 00 9300 --%%-- s --%%-- z 21-07-15 293 01 3293 1598162004 ORD xf 28-01-16 370 01 4370 1475630204 x 29-05-14 20 01 0084 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 21 01 0046 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 22 01 0018 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 23 01 0830 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 30 01 0104 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 40 01 0007 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 60 01 0705 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 63 01 3401 E-Book http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 LF 70 01 0089 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 105 01 0841 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 110 01 3110 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 132 01 0959 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 151 01 0546 Volltext http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 161 01 0960 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 285 01 0517 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 293 01 3293 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 370 01 4370 http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777 60 01 0705 10 ho 285 00 DE-517 00 TF 04999 285 00 DE-517 00 AR 14120 285 00 DE-517 00 SK 830 285 00 DE-517 00 UT 1800 20 01 0084 OLRD 110 01 3110 OLRD 370 01 4370 OLRD 22 01 0018 SUBolrd 23 01 0830 olr-d 60 01 0705 OLRD 63 01 3401 ORD 132 01 0959 OLR-DISS 151 01 0546 OLR-ODISS 161 01 0960 ORD 293 01 3293 ORD 23 01 0830 2014-05-29:01:06:56 |
language |
English |
format_phy_str_mv |
Book |
bklname |
Regionale Umweltprobleme Wahrscheinlichkeitsrechnung |
institution |
findex.gbv.de |
selectbib_iln_str_mv |
20@ 21@ 22@u 23@ 30@ 40@sn 60@ 63@ 70@do 105@ 110@ 132@ 151@ 161@ 285@ 293@f 370@ |
dewey-raw |
550 |
authorswithroles_txt_mv |
Vogel, Kristin @@aut@@ Scherbaum, Frank @@oth@@ |
genre_facet |
Hochschulschrift |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000cam a2200265 4500</leader><controlfield tag="001">782264476</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230324224533.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">140402s2014 gw |||||om 00| ||eng c</controlfield><datafield tag="015" ind1=" " ind2=" "><subfield code="a">14,O04</subfield><subfield code="2">dnb</subfield></datafield><datafield tag="016" ind1="7" ind2=" "><subfield code="a">1048476014</subfield><subfield code="2">DE-101</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">urn:nbn:de:kobv:517-opus-69777</subfield><subfield code="2">urn</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)782264476</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DNB1048476014</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)875594439</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="044" ind1=" " ind2=" "><subfield code="c">XA-DE-BB</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">550</subfield><subfield code="q">DNB</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">43.48</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">31.70</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Vogel, Kristin</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)1074045890</subfield><subfield code="0">(DE-627)830216421</subfield><subfield code="0">(DE-576)435559109</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Applications of Bayesian networks in natural hazard assessments</subfield><subfield code="c">Kristin Vogel. Betreuer: Frank Scherbaum</subfield></datafield><datafield tag="246" ind1="1" ind2=" "><subfield code="i">Parallelsacht.</subfield><subfield code="a">Anwendungen von Bayes'schen Netzen bei der Einschätzung von Naturgefahren</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2014</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">Online-Ressource (PDF-Datei: 11441 KB, X, 84 Bl.)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="502" ind1=" " ind2=" "><subfield code="a">Potsdam, Univ., Kumulative Diss., 2014</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Even though quite different in occurrence and consequences, from a modeling perspective many natural hazards share similar properties and challenges. Their complex nature as well as lacking knowledge about their driving forces and potential effects make their analysis demanding: uncertainty about the modeling framework, inaccurate or incomplete event observations and the intrinsic randomness of the natural phenomenon add up to different interacting layers of uncertainty, which require a careful handling. Nevertheless deterministic approaches are still widely used in natural hazard assessments, holding the risk of underestimating the hazard with disastrous effects. The all-round probabilistic framework of Bayesian networks constitutes an attractive alternative. In contrast to deterministic proceedings, it treats response variables as well as explanatory variables as random variables making no difference between input and output variables. Using a graphical representation Bayesian networks encode the dependency relations between the variables in a directed acyclic graph: variables are represented as nodes and (in-)dependencies between variables as (missing) edges between the nodes. The joint distribution of all variables can thus be described by decomposing it, according to the depicted independences, into a product of local conditional probability distributions, which are defined by the parameters of the Bayesian network. In the framework of this thesis the Bayesian network approach is applied to different natural hazard domains (i.e. seismic hazard, flood damage and landslide assessments). Learning the network structure and parameters from data, Bayesian networks reveal relevant dependency relations between the included variables and help to gain knowledge about the underlying processes. The problem of Bayesian network learning is cast in a Bayesian framework, considering the network structure and parameters as random variables itself and searching for the most likely combination of both, which corresponds to the maximum a posteriori (MAP score) of their joint distribution given the observed data. Although well studied in theory the learning of Bayesian networks based on real-world data is usually not straight forward and requires an adoption of existing algorithms. Typically arising problems are the handling of continuous variables, incomplete observations and the interaction of both. Working with continuous distributions requires assumptions about the allowed families of distributions. To "let the data speak" and avoid wrong assumptions, continuous variables are instead discretized here, thus allowing for a completely data-driven and distribution-free learning. An extension of the MAP score, considering the discretization as random variable as well, is developed for an automatic multivariate discretization, that takes interactions between the variables into account. The discretization process is nested into the network learning and requires several iterations. Having to face incomplete observations on top, this may pose a computational burden. Iterative proceedings for missing value estimation become quickly infeasible. A more efficient albeit approximate method is used instead, estimating the missing values based only on the observations of variables directly interacting with the missing variable. Moreover natural hazard assessments often have a primary interest in a certain target variable. The discretization learned for this variable does not always have the required resolution for a good prediction performance. Finer resolutions for (conditional) continuous distributions are achieved with continuous approximations subsequent to the Bayesian network learning, using kernel density estimations or mixtures of truncated exponential functions. All our proceedings are completely data-driven. We thus avoid assumptions that require expert knowledge and instead provide domain independent solutions, that are applicable not only in other natural hazard assessments, but in a variety of domains struggling with uncertainties.</subfield></datafield><datafield tag="583" ind1="1" ind2=" "><subfield code="z">Langzeitarchivierung gewährleistet</subfield><subfield code="2">pdager</subfield></datafield><datafield tag="655" ind1=" " ind2="7"><subfield code="a">Hochschulschrift</subfield><subfield code="0">(DE-588)4113937-9</subfield><subfield code="0">(DE-627)105825778</subfield><subfield code="0">(DE-576)209480580</subfield><subfield code="2">gnd-content</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="D">s</subfield><subfield code="0">(DE-588)4123823-0</subfield><subfield code="0">(DE-627)105752304</subfield><subfield code="0">(DE-576)209562978</subfield><subfield code="a">Naturgefahr</subfield><subfield code="2">gnd</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="D">s</subfield><subfield code="0">(DE-588)4137042-9</subfield><subfield code="0">(DE-627)10427932X</subfield><subfield code="0">(DE-576)209674318</subfield><subfield code="a">Risikoanalyse</subfield><subfield code="2">gnd</subfield></datafield><datafield tag="689" ind1="0" ind2="2"><subfield code="D">s</subfield><subfield code="0">(DE-588)4567228-3</subfield><subfield code="0">(DE-627)306094517</subfield><subfield code="0">(DE-576)213783134</subfield><subfield code="a">Bayes-Netz</subfield><subfield code="2">gnd</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">(DE-627)</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Scherbaum, Frank</subfield><subfield code="e">betreuer</subfield><subfield code="4">oth</subfield></datafield><datafield tag="751" ind1=" " ind2=" "><subfield code="a">Potsdam</subfield><subfield code="4">uvp</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Druckausg.</subfield><subfield code="a">Vogel, Kristin</subfield><subfield code="t">Applications of Bayesian networks in natural hazard assessments</subfield><subfield code="d">Potsdam, 2013</subfield><subfield code="h">x, 84 Blätter</subfield><subfield code="w">(DE-627)830342885</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777</subfield><subfield code="v">2014-04-02</subfield><subfield code="x">Resolving-System</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://d-nb.info/1048476014/34</subfield><subfield code="v">2014-04-02</subfield><subfield code="x">Langzeitarchivierung Nationalbibliothek</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://opus.kobv.de/ubp/volltexte/2014/6977/</subfield><subfield code="v">2014-04-02</subfield><subfield code="x">Verlag</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV-ODiss</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-84</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_1</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_KXP</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-GGO</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_21</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-46</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-18</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-830</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_30</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-104</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-7</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-705</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-Wim2</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-89</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-841</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-Luen4</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_132</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-959</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-546</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-960</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-517</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-960-3</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-1373</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">43.48</subfield><subfield code="j">Regionale Umweltprobleme</subfield><subfield code="0">(DE-627)181569981</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">31.70</subfield><subfield code="j">Wahrscheinlichkeitsrechnung</subfield><subfield code="0">(DE-627)106408070</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">BO</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">20</subfield><subfield code="1">01</subfield><subfield code="x">0084</subfield><subfield code="b">1475518501</subfield><subfield code="y">x</subfield><subfield code="z">29-05-14</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">21</subfield><subfield code="1">01</subfield><subfield code="x">0046</subfield><subfield code="b">1475536720</subfield><subfield code="y">z</subfield><subfield code="z">29-05-14</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">22</subfield><subfield code="1">01</subfield><subfield code="x">0018</subfield><subfield code="b">1475554664</subfield><subfield code="h">SUBolrd</subfield><subfield code="y">xu</subfield><subfield code="z">29-05-14</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">23</subfield><subfield code="1">01</subfield><subfield code="x">0830</subfield><subfield code="b">1475568061</subfield><subfield code="h">olr-d</subfield><subfield code="y">x</subfield><subfield code="z">29-05-14</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">30</subfield><subfield code="1">01</subfield><subfield code="x">0104</subfield><subfield code="b">1475578156</subfield><subfield code="y">z</subfield><subfield code="z">29-05-14</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">40</subfield><subfield code="1">01</subfield><subfield code="x">0007</subfield><subfield code="b">1475590288</subfield><subfield code="y">xsn</subfield><subfield code="z">29-05-14</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">60</subfield><subfield code="1">01</subfield><subfield code="x">0705</subfield><subfield code="b">1475608004</subfield><subfield code="h">OLRD</subfield><subfield code="y">z</subfield><subfield code="z">29-05-14</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">63</subfield><subfield code="1">01</subfield><subfield code="x">3401</subfield><subfield code="b">1595845496</subfield><subfield code="h">ORD</subfield><subfield code="y">x</subfield><subfield code="z">21-01-16</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">70</subfield><subfield code="1">01</subfield><subfield code="x">0089</subfield><subfield code="b">1475634854</subfield><subfield code="y">zdo</subfield><subfield code="z">29-05-14</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">105</subfield><subfield code="1">01</subfield><subfield code="x">0841</subfield><subfield code="b">1596657936</subfield><subfield code="y">z</subfield><subfield code="z">21-01-16</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">110</subfield><subfield code="1">01</subfield><subfield code="x">3110</subfield><subfield code="b">1475623658</subfield><subfield code="y">x</subfield><subfield code="z">29-05-14</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">132</subfield><subfield code="1">01</subfield><subfield code="x">0959</subfield><subfield code="b">1498549047</subfield><subfield code="h">OLR-DISS</subfield><subfield code="y">x</subfield><subfield code="z">14-08-14</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">151</subfield><subfield code="1">01</subfield><subfield code="x">0546</subfield><subfield code="b">3594257711</subfield><subfield code="h">OLR-ODISS</subfield><subfield code="y">z</subfield><subfield code="z">13-02-20</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">161</subfield><subfield code="1">01</subfield><subfield code="x">0960</subfield><subfield code="b">1597648639</subfield><subfield code="h">ORD</subfield><subfield code="y">x</subfield><subfield code="z">28-01-16</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">285</subfield><subfield code="1">01</subfield><subfield code="x">0517</subfield><subfield code="b">1554521785</subfield><subfield code="c">00</subfield><subfield code="f">9300</subfield><subfield code="d">--%%--</subfield><subfield code="e">s</subfield><subfield code="j">--%%--</subfield><subfield code="y">z</subfield><subfield code="z">21-07-15</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">293</subfield><subfield code="1">01</subfield><subfield code="x">3293</subfield><subfield code="b">1598162004</subfield><subfield code="h">ORD</subfield><subfield code="y">xf</subfield><subfield code="z">28-01-16</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">370</subfield><subfield code="1">01</subfield><subfield code="x">4370</subfield><subfield code="b">1475630204</subfield><subfield code="y">x</subfield><subfield code="z">29-05-14</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">20</subfield><subfield code="1">01</subfield><subfield code="x">0084</subfield><subfield code="r">http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">21</subfield><subfield code="1">01</subfield><subfield code="x">0046</subfield><subfield code="r">http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">22</subfield><subfield code="1">01</subfield><subfield code="x">0018</subfield><subfield code="r">http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">23</subfield><subfield code="1">01</subfield><subfield code="x">0830</subfield><subfield code="r">http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">30</subfield><subfield code="1">01</subfield><subfield code="x">0104</subfield><subfield code="r">http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">40</subfield><subfield code="1">01</subfield><subfield code="x">0007</subfield><subfield code="r">http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">60</subfield><subfield code="1">01</subfield><subfield code="x">0705</subfield><subfield code="r">http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">63</subfield><subfield code="1">01</subfield><subfield code="x">3401</subfield><subfield code="y">E-Book</subfield><subfield code="r">http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777</subfield><subfield code="z">LF</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">70</subfield><subfield code="1">01</subfield><subfield code="x">0089</subfield><subfield code="r">http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">105</subfield><subfield code="1">01</subfield><subfield code="x">0841</subfield><subfield code="r">http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">110</subfield><subfield code="1">01</subfield><subfield code="x">3110</subfield><subfield code="r">http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">132</subfield><subfield code="1">01</subfield><subfield code="x">0959</subfield><subfield code="r">http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">151</subfield><subfield code="1">01</subfield><subfield code="x">0546</subfield><subfield code="y">Volltext</subfield><subfield code="r">http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">161</subfield><subfield code="1">01</subfield><subfield code="x">0960</subfield><subfield code="r">http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">285</subfield><subfield code="1">01</subfield><subfield code="x">0517</subfield><subfield code="r">http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">293</subfield><subfield code="1">01</subfield><subfield code="x">3293</subfield><subfield code="r">http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">370</subfield><subfield code="1">01</subfield><subfield code="x">4370</subfield><subfield code="r">http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777</subfield></datafield><datafield tag="983" ind1=" " ind2=" "><subfield code="2">60</subfield><subfield code="1">01</subfield><subfield code="x">0705</subfield><subfield code="8">10</subfield><subfield code="a">ho</subfield></datafield><datafield tag="983" ind1=" " ind2=" "><subfield code="2">285</subfield><subfield code="1">00</subfield><subfield code="x">DE-517</subfield><subfield code="8">00</subfield><subfield code="a">TF 04999</subfield></datafield><datafield tag="983" ind1=" " ind2=" "><subfield code="2">285</subfield><subfield code="1">00</subfield><subfield code="x">DE-517</subfield><subfield code="8">00</subfield><subfield code="a">AR 14120</subfield></datafield><datafield tag="983" ind1=" " ind2=" "><subfield code="2">285</subfield><subfield code="1">00</subfield><subfield code="x">DE-517</subfield><subfield code="8">00</subfield><subfield code="a">SK 830</subfield></datafield><datafield tag="983" ind1=" " ind2=" "><subfield code="2">285</subfield><subfield code="1">00</subfield><subfield code="x">DE-517</subfield><subfield code="8">00</subfield><subfield code="a">UT 1800</subfield></datafield><datafield tag="985" ind1=" " ind2=" "><subfield code="2">20</subfield><subfield code="1">01</subfield><subfield code="x">0084</subfield><subfield code="a">OLRD</subfield></datafield><datafield tag="985" ind1=" " ind2=" "><subfield code="2">110</subfield><subfield code="1">01</subfield><subfield code="x">3110</subfield><subfield code="a">OLRD</subfield></datafield><datafield tag="985" ind1=" " ind2=" "><subfield code="2">370</subfield><subfield code="1">01</subfield><subfield code="x">4370</subfield><subfield code="a">OLRD</subfield></datafield><datafield tag="995" ind1=" " ind2=" "><subfield code="2">22</subfield><subfield code="1">01</subfield><subfield code="x">0018</subfield><subfield code="a">SUBolrd</subfield></datafield><datafield tag="995" ind1=" " ind2=" "><subfield code="2">23</subfield><subfield code="1">01</subfield><subfield code="x">0830</subfield><subfield code="a">olr-d</subfield></datafield><datafield tag="995" ind1=" " ind2=" "><subfield code="2">60</subfield><subfield code="1">01</subfield><subfield code="x">0705</subfield><subfield code="a">OLRD</subfield></datafield><datafield tag="995" ind1=" " ind2=" "><subfield code="2">63</subfield><subfield code="1">01</subfield><subfield code="x">3401</subfield><subfield code="a">ORD</subfield></datafield><datafield tag="995" ind1=" " ind2=" "><subfield code="2">132</subfield><subfield code="1">01</subfield><subfield code="x">0959</subfield><subfield code="a">OLR-DISS</subfield></datafield><datafield tag="995" ind1=" " ind2=" "><subfield code="2">151</subfield><subfield code="1">01</subfield><subfield code="x">0546</subfield><subfield code="a">OLR-ODISS</subfield></datafield><datafield tag="995" ind1=" " ind2=" "><subfield code="2">161</subfield><subfield code="1">01</subfield><subfield code="x">0960</subfield><subfield code="a">ORD</subfield></datafield><datafield tag="995" ind1=" " ind2=" "><subfield code="2">293</subfield><subfield code="1">01</subfield><subfield code="x">3293</subfield><subfield code="a">ORD</subfield></datafield><datafield tag="998" ind1=" " ind2=" "><subfield code="2">23</subfield><subfield code="1">01</subfield><subfield code="x">0830</subfield><subfield code="0">2014-05-29:01:06:56</subfield></datafield></record></collection>
|
standort_iln_str_mv |
285:9300 0517:9300 |
author |
Vogel, Kristin |
spellingShingle |
Vogel, Kristin ddc 550 bkl 43.48 bkl 31.70 gnd Naturgefahr gnd Risikoanalyse gnd Bayes-Netz Applications of Bayesian networks in natural hazard assessments |
authorStr |
Vogel, Kristin |
ppnlink_with_tag_str_mv |
@@776@@(DE-627)830342885 |
format |
eBook |
typewithnormlink_str_mv |
DifferentiatedPerson@(DE-588)1074045890 Person@(DE-588)1074045890 SubjectHeadingSensoStricto@(DE-588)4113937-9 SubjectHeading@(DE-588)4113937-9 SubjectHeadingSensoStricto@(DE-588)4123823-0 SubjectHeading@(DE-588)4123823-0 SubjectHeading@(DE-588)4137042-9 SubjectHeadingSensoStricto@(DE-588)4137042-9 SubjectHeadingSensoStricto@(DE-588)4567228-3 SubjectHeading@(DE-588)4567228-3 |
collection |
KXP GVK |
remote_str |
true |
abrufzeichen_iln_str_mv |
22@SUBolrd 23@olr-d 60@OLRD 63@ORD 132@OLR-DISS 151@OLR-ODISS 161@ORD 293@ORD |
abrufzeichen_iln_scis_mv |
22@SUBolrd 23@olr-d 60@OLRD 63@ORD 132@OLR-DISS 151@OLR-ODISS 161@ORD 293@ORD |
notation_local_iln_str_mv |
60:ho 0705:ho 285:TF 04999 DE-517:TF 04999 285:AR 14120 DE-517:AR 14120 285:SK 830 DE-517:SK 830 285:UT 1800 DE-517:UT 1800 |
topic |
ddc 550 bkl 43.48 bkl 31.70 gnd Naturgefahr gnd Risikoanalyse gnd Bayes-Netz |
topic_unstemmed |
ddc 550 bkl 43.48 bkl 31.70 gnd Naturgefahr gnd Risikoanalyse gnd Bayes-Netz |
topic_browse |
ddc 550 bkl 43.48 bkl 31.70 gnd Naturgefahr gnd Risikoanalyse gnd Bayes-Netz |
format_main_str_mv |
Text Buch |
format_details_str_mv |
Hochschulschrift |
carriertype_str_mv |
cr |
author2_variant |
f s fs |
normlinkwithtype_str_mv |
(DE-588)1074045890@DifferentiatedPerson (DE-588)1074045890@Person (DE-588)4113937-9@SubjectHeadingSensoStricto (DE-588)4113937-9@SubjectHeading (DE-588)4123823-0@SubjectHeadingSensoStricto (DE-588)4123823-0@SubjectHeading (DE-588)4137042-9@SubjectHeading (DE-588)4137042-9@SubjectHeadingSensoStricto (DE-588)4567228-3@SubjectHeadingSensoStricto (DE-588)4567228-3@SubjectHeading |
dewey-tens |
550 - Earth sciences & geology |
title |
Applications of Bayesian networks in natural hazard assessments |
callnumber-first-code |
- |
lang_code |
eng |
class_local |
60 01 0705 10 ho 285 00 DE-517 00 TF 04999 285 00 DE-517 00 AR 14120 285 00 DE-517 00 SK 830 285 00 DE-517 00 UT 1800 |
selektneu_str_mv |
23@2014-05-29:01:06:56 |
dewey-hundreds |
500 - Science |
recordtype |
marc |
contenttype_str_mv |
txt |
class_local_iln |
60:ho 0705:ho 285:TF 04999 DE-517:TF 04999 285:AR 14120 DE-517:AR 14120 285:SK 830 DE-517:SK 830 285:UT 1800 DE-517:UT 1800 |
author_browse |
Vogel, Kristin |
physical |
Online-Ressource (PDF-Datei: 11441 KB, X, 84 Bl.) |
class |
550 DNB 43.48 bkl 31.70 bkl 60 01 0705 10 ho 285 00 DE-517 00 TF 04999 285 00 DE-517 00 AR 14120 285 00 DE-517 00 SK 830 285 00 DE-517 00 UT 1800 |
classname_local_iln_str_mv |
60: 0705: 285: DE-517: |
normlink |
1074045890 830216421 435559109 4113937-9 105825778 209480580 4123823-0 105752304 209562978 4137042-9 10427932X 209674318 4567228-3 306094517 213783134 181569981 106408070 2014-05-29:01:06:56 |
normlink_prefix_str_mv |
(DE-588)1074045890 (DE-627)830216421 (DE-576)435559109 (DE-588)4113937-9 (DE-627)105825778 (DE-576)209480580 (DE-588)4123823-0 (DE-627)105752304 (DE-576)209562978 (DE-588)4137042-9 (DE-627)10427932X (DE-576)209674318 (DE-588)4567228-3 (DE-627)306094517 (DE-576)213783134 (DE-627)181569981 (DE-627)106408070 2014-05-29:01:06:56 |
collection_details |
GBV-ODiss GBV_ILN_20 ISIL_DE-84 SYSFLAG_1 GBV_KXP SSG-OPC-GGO GBV_ILN_21 ISIL_DE-46 GBV_ILN_22 ISIL_DE-18 GBV_ILN_23 ISIL_DE-830 GBV_ILN_30 ISIL_DE-104 GBV_ILN_40 ISIL_DE-7 GBV_ILN_60 ISIL_DE-705 GBV_ILN_63 ISIL_DE-Wim2 GBV_ILN_70 ISIL_DE-89 GBV_ILN_105 ISIL_DE-841 GBV_ILN_110 ISIL_DE-Luen4 GBV_ILN_132 ISIL_DE-959 GBV_ILN_151 ISIL_DE-546 GBV_ILN_161 ISIL_DE-960 GBV_ILN_285 ISIL_DE-517 GBV_ILN_293 ISIL_DE-960-3 GBV_ILN_370 ISIL_DE-1373 |
title_short |
Applications of Bayesian networks in natural hazard assessments |
ausleihindikator_str_mv |
20 21 22 23 30 40 60 63 70 105 110 132 151 161 285:s 293 370 |
rolewithnormlink_str_mv |
@@aut@@(DE-588)1074045890 @@655@@(DE-588)4113937-9 @@689@@(DE-588)4123823-0 @@689@@(DE-588)4137042-9 @@689@@(DE-588)4567228-3 |
remote_bool |
true |
GND_str_mv |
Vogel, Kristin Hochschulschrift Naturgefahr Risikobewertung Risiko / Analyse Risk assessment Risikobeurteilung Risk analysis Risikoeinschätzung Risikoabschätzung Risikoanalyse Bayesian Network Bayes Net Bayessches Netz Bayes-Netz |
GND_txt_mv |
Vogel, Kristin Hochschulschrift Naturgefahr Risikobewertung Risiko / Analyse Risk assessment Risikobeurteilung Risk analysis Risikoeinschätzung Risikoabschätzung Risikoanalyse Bayesian Network Bayes Net Bayessches Netz Bayes-Netz |
GND_txtF_mv |
Vogel, Kristin Hochschulschrift Naturgefahr Risikobewertung Risiko / Analyse Risk assessment Risikobeurteilung Risk analysis Risikoeinschätzung Risikoabschätzung Risikoanalyse Bayesian Network Bayes Net Bayessches Netz Bayes-Netz |
title_alt |
Anwendungen von Bayes'schen Netzen bei der Einschätzung von Naturgefahren |
callnumber-a |
--%%-- |
up_date |
2024-07-04T14:12:24.294Z |
_version_ |
1803658035775668224 |
fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000cam a2200265 4500</leader><controlfield tag="001">782264476</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230324224533.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">140402s2014 gw |||||om 00| ||eng c</controlfield><datafield tag="015" ind1=" " ind2=" "><subfield code="a">14,O04</subfield><subfield code="2">dnb</subfield></datafield><datafield tag="016" ind1="7" ind2=" "><subfield code="a">1048476014</subfield><subfield code="2">DE-101</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">urn:nbn:de:kobv:517-opus-69777</subfield><subfield code="2">urn</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)782264476</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DNB1048476014</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)875594439</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="044" ind1=" " ind2=" "><subfield code="c">XA-DE-BB</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">550</subfield><subfield code="q">DNB</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">43.48</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">31.70</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Vogel, Kristin</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)1074045890</subfield><subfield code="0">(DE-627)830216421</subfield><subfield code="0">(DE-576)435559109</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Applications of Bayesian networks in natural hazard assessments</subfield><subfield code="c">Kristin Vogel. Betreuer: Frank Scherbaum</subfield></datafield><datafield tag="246" ind1="1" ind2=" "><subfield code="i">Parallelsacht.</subfield><subfield code="a">Anwendungen von Bayes'schen Netzen bei der Einschätzung von Naturgefahren</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2014</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">Online-Ressource (PDF-Datei: 11441 KB, X, 84 Bl.)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="502" ind1=" " ind2=" "><subfield code="a">Potsdam, Univ., Kumulative Diss., 2014</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Even though quite different in occurrence and consequences, from a modeling perspective many natural hazards share similar properties and challenges. Their complex nature as well as lacking knowledge about their driving forces and potential effects make their analysis demanding: uncertainty about the modeling framework, inaccurate or incomplete event observations and the intrinsic randomness of the natural phenomenon add up to different interacting layers of uncertainty, which require a careful handling. Nevertheless deterministic approaches are still widely used in natural hazard assessments, holding the risk of underestimating the hazard with disastrous effects. The all-round probabilistic framework of Bayesian networks constitutes an attractive alternative. In contrast to deterministic proceedings, it treats response variables as well as explanatory variables as random variables making no difference between input and output variables. Using a graphical representation Bayesian networks encode the dependency relations between the variables in a directed acyclic graph: variables are represented as nodes and (in-)dependencies between variables as (missing) edges between the nodes. The joint distribution of all variables can thus be described by decomposing it, according to the depicted independences, into a product of local conditional probability distributions, which are defined by the parameters of the Bayesian network. In the framework of this thesis the Bayesian network approach is applied to different natural hazard domains (i.e. seismic hazard, flood damage and landslide assessments). Learning the network structure and parameters from data, Bayesian networks reveal relevant dependency relations between the included variables and help to gain knowledge about the underlying processes. The problem of Bayesian network learning is cast in a Bayesian framework, considering the network structure and parameters as random variables itself and searching for the most likely combination of both, which corresponds to the maximum a posteriori (MAP score) of their joint distribution given the observed data. Although well studied in theory the learning of Bayesian networks based on real-world data is usually not straight forward and requires an adoption of existing algorithms. Typically arising problems are the handling of continuous variables, incomplete observations and the interaction of both. Working with continuous distributions requires assumptions about the allowed families of distributions. To "let the data speak" and avoid wrong assumptions, continuous variables are instead discretized here, thus allowing for a completely data-driven and distribution-free learning. An extension of the MAP score, considering the discretization as random variable as well, is developed for an automatic multivariate discretization, that takes interactions between the variables into account. The discretization process is nested into the network learning and requires several iterations. Having to face incomplete observations on top, this may pose a computational burden. Iterative proceedings for missing value estimation become quickly infeasible. A more efficient albeit approximate method is used instead, estimating the missing values based only on the observations of variables directly interacting with the missing variable. Moreover natural hazard assessments often have a primary interest in a certain target variable. The discretization learned for this variable does not always have the required resolution for a good prediction performance. Finer resolutions for (conditional) continuous distributions are achieved with continuous approximations subsequent to the Bayesian network learning, using kernel density estimations or mixtures of truncated exponential functions. All our proceedings are completely data-driven. We thus avoid assumptions that require expert knowledge and instead provide domain independent solutions, that are applicable not only in other natural hazard assessments, but in a variety of domains struggling with uncertainties.</subfield></datafield><datafield tag="583" ind1="1" ind2=" "><subfield code="z">Langzeitarchivierung gewährleistet</subfield><subfield code="2">pdager</subfield></datafield><datafield tag="655" ind1=" " ind2="7"><subfield code="a">Hochschulschrift</subfield><subfield code="0">(DE-588)4113937-9</subfield><subfield code="0">(DE-627)105825778</subfield><subfield code="0">(DE-576)209480580</subfield><subfield code="2">gnd-content</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="D">s</subfield><subfield code="0">(DE-588)4123823-0</subfield><subfield code="0">(DE-627)105752304</subfield><subfield code="0">(DE-576)209562978</subfield><subfield code="a">Naturgefahr</subfield><subfield code="2">gnd</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="D">s</subfield><subfield code="0">(DE-588)4137042-9</subfield><subfield code="0">(DE-627)10427932X</subfield><subfield code="0">(DE-576)209674318</subfield><subfield code="a">Risikoanalyse</subfield><subfield code="2">gnd</subfield></datafield><datafield tag="689" ind1="0" ind2="2"><subfield code="D">s</subfield><subfield code="0">(DE-588)4567228-3</subfield><subfield code="0">(DE-627)306094517</subfield><subfield code="0">(DE-576)213783134</subfield><subfield code="a">Bayes-Netz</subfield><subfield code="2">gnd</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">(DE-627)</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Scherbaum, Frank</subfield><subfield code="e">betreuer</subfield><subfield code="4">oth</subfield></datafield><datafield tag="751" ind1=" " ind2=" "><subfield code="a">Potsdam</subfield><subfield code="4">uvp</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Druckausg.</subfield><subfield code="a">Vogel, Kristin</subfield><subfield code="t">Applications of Bayesian networks in natural hazard assessments</subfield><subfield code="d">Potsdam, 2013</subfield><subfield code="h">x, 84 Blätter</subfield><subfield code="w">(DE-627)830342885</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777</subfield><subfield code="v">2014-04-02</subfield><subfield code="x">Resolving-System</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://d-nb.info/1048476014/34</subfield><subfield code="v">2014-04-02</subfield><subfield code="x">Langzeitarchivierung Nationalbibliothek</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://opus.kobv.de/ubp/volltexte/2014/6977/</subfield><subfield code="v">2014-04-02</subfield><subfield code="x">Verlag</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV-ODiss</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-84</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_1</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_KXP</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-GGO</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_21</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-46</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-18</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-830</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_30</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-104</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-7</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-705</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-Wim2</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-89</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-841</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-Luen4</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_132</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-959</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-546</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-960</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-517</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-960-3</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-1373</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">43.48</subfield><subfield code="j">Regionale Umweltprobleme</subfield><subfield code="0">(DE-627)181569981</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">31.70</subfield><subfield code="j">Wahrscheinlichkeitsrechnung</subfield><subfield code="0">(DE-627)106408070</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">BO</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">20</subfield><subfield code="1">01</subfield><subfield code="x">0084</subfield><subfield code="b">1475518501</subfield><subfield code="y">x</subfield><subfield code="z">29-05-14</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">21</subfield><subfield code="1">01</subfield><subfield code="x">0046</subfield><subfield code="b">1475536720</subfield><subfield code="y">z</subfield><subfield code="z">29-05-14</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">22</subfield><subfield code="1">01</subfield><subfield code="x">0018</subfield><subfield code="b">1475554664</subfield><subfield code="h">SUBolrd</subfield><subfield code="y">xu</subfield><subfield code="z">29-05-14</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">23</subfield><subfield code="1">01</subfield><subfield code="x">0830</subfield><subfield code="b">1475568061</subfield><subfield code="h">olr-d</subfield><subfield code="y">x</subfield><subfield code="z">29-05-14</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">30</subfield><subfield code="1">01</subfield><subfield code="x">0104</subfield><subfield code="b">1475578156</subfield><subfield code="y">z</subfield><subfield code="z">29-05-14</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">40</subfield><subfield code="1">01</subfield><subfield code="x">0007</subfield><subfield code="b">1475590288</subfield><subfield code="y">xsn</subfield><subfield code="z">29-05-14</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">60</subfield><subfield code="1">01</subfield><subfield code="x">0705</subfield><subfield code="b">1475608004</subfield><subfield code="h">OLRD</subfield><subfield code="y">z</subfield><subfield code="z">29-05-14</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">63</subfield><subfield code="1">01</subfield><subfield code="x">3401</subfield><subfield code="b">1595845496</subfield><subfield code="h">ORD</subfield><subfield code="y">x</subfield><subfield code="z">21-01-16</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">70</subfield><subfield code="1">01</subfield><subfield code="x">0089</subfield><subfield code="b">1475634854</subfield><subfield code="y">zdo</subfield><subfield code="z">29-05-14</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">105</subfield><subfield code="1">01</subfield><subfield code="x">0841</subfield><subfield code="b">1596657936</subfield><subfield code="y">z</subfield><subfield code="z">21-01-16</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">110</subfield><subfield code="1">01</subfield><subfield code="x">3110</subfield><subfield code="b">1475623658</subfield><subfield code="y">x</subfield><subfield code="z">29-05-14</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">132</subfield><subfield code="1">01</subfield><subfield code="x">0959</subfield><subfield code="b">1498549047</subfield><subfield code="h">OLR-DISS</subfield><subfield code="y">x</subfield><subfield code="z">14-08-14</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">151</subfield><subfield code="1">01</subfield><subfield code="x">0546</subfield><subfield code="b">3594257711</subfield><subfield code="h">OLR-ODISS</subfield><subfield code="y">z</subfield><subfield code="z">13-02-20</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">161</subfield><subfield code="1">01</subfield><subfield code="x">0960</subfield><subfield code="b">1597648639</subfield><subfield code="h">ORD</subfield><subfield code="y">x</subfield><subfield code="z">28-01-16</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">285</subfield><subfield code="1">01</subfield><subfield code="x">0517</subfield><subfield code="b">1554521785</subfield><subfield code="c">00</subfield><subfield code="f">9300</subfield><subfield code="d">--%%--</subfield><subfield code="e">s</subfield><subfield code="j">--%%--</subfield><subfield code="y">z</subfield><subfield code="z">21-07-15</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">293</subfield><subfield code="1">01</subfield><subfield code="x">3293</subfield><subfield code="b">1598162004</subfield><subfield code="h">ORD</subfield><subfield code="y">xf</subfield><subfield code="z">28-01-16</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">370</subfield><subfield code="1">01</subfield><subfield code="x">4370</subfield><subfield code="b">1475630204</subfield><subfield code="y">x</subfield><subfield code="z">29-05-14</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">20</subfield><subfield code="1">01</subfield><subfield code="x">0084</subfield><subfield code="r">http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">21</subfield><subfield code="1">01</subfield><subfield code="x">0046</subfield><subfield code="r">http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">22</subfield><subfield code="1">01</subfield><subfield code="x">0018</subfield><subfield code="r">http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">23</subfield><subfield code="1">01</subfield><subfield code="x">0830</subfield><subfield code="r">http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">30</subfield><subfield code="1">01</subfield><subfield code="x">0104</subfield><subfield code="r">http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">40</subfield><subfield code="1">01</subfield><subfield code="x">0007</subfield><subfield code="r">http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">60</subfield><subfield code="1">01</subfield><subfield code="x">0705</subfield><subfield code="r">http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">63</subfield><subfield code="1">01</subfield><subfield code="x">3401</subfield><subfield code="y">E-Book</subfield><subfield code="r">http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777</subfield><subfield code="z">LF</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">70</subfield><subfield code="1">01</subfield><subfield code="x">0089</subfield><subfield code="r">http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">105</subfield><subfield code="1">01</subfield><subfield code="x">0841</subfield><subfield code="r">http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">110</subfield><subfield code="1">01</subfield><subfield code="x">3110</subfield><subfield code="r">http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">132</subfield><subfield code="1">01</subfield><subfield code="x">0959</subfield><subfield code="r">http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">151</subfield><subfield code="1">01</subfield><subfield code="x">0546</subfield><subfield code="y">Volltext</subfield><subfield code="r">http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">161</subfield><subfield code="1">01</subfield><subfield code="x">0960</subfield><subfield code="r">http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">285</subfield><subfield code="1">01</subfield><subfield code="x">0517</subfield><subfield code="r">http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">293</subfield><subfield code="1">01</subfield><subfield code="x">3293</subfield><subfield code="r">http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">370</subfield><subfield code="1">01</subfield><subfield code="x">4370</subfield><subfield code="r">http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777</subfield></datafield><datafield tag="983" ind1=" " ind2=" "><subfield code="2">60</subfield><subfield code="1">01</subfield><subfield code="x">0705</subfield><subfield code="8">10</subfield><subfield code="a">ho</subfield></datafield><datafield tag="983" ind1=" " ind2=" "><subfield code="2">285</subfield><subfield code="1">00</subfield><subfield code="x">DE-517</subfield><subfield code="8">00</subfield><subfield code="a">TF 04999</subfield></datafield><datafield tag="983" ind1=" " ind2=" "><subfield code="2">285</subfield><subfield code="1">00</subfield><subfield code="x">DE-517</subfield><subfield code="8">00</subfield><subfield code="a">AR 14120</subfield></datafield><datafield tag="983" ind1=" " ind2=" "><subfield code="2">285</subfield><subfield code="1">00</subfield><subfield code="x">DE-517</subfield><subfield code="8">00</subfield><subfield code="a">SK 830</subfield></datafield><datafield tag="983" ind1=" " ind2=" "><subfield code="2">285</subfield><subfield code="1">00</subfield><subfield code="x">DE-517</subfield><subfield code="8">00</subfield><subfield code="a">UT 1800</subfield></datafield><datafield tag="985" ind1=" " ind2=" "><subfield code="2">20</subfield><subfield code="1">01</subfield><subfield code="x">0084</subfield><subfield code="a">OLRD</subfield></datafield><datafield tag="985" ind1=" " ind2=" "><subfield code="2">110</subfield><subfield code="1">01</subfield><subfield code="x">3110</subfield><subfield code="a">OLRD</subfield></datafield><datafield tag="985" ind1=" " ind2=" "><subfield code="2">370</subfield><subfield code="1">01</subfield><subfield code="x">4370</subfield><subfield code="a">OLRD</subfield></datafield><datafield tag="995" ind1=" " ind2=" "><subfield code="2">22</subfield><subfield code="1">01</subfield><subfield code="x">0018</subfield><subfield code="a">SUBolrd</subfield></datafield><datafield tag="995" ind1=" " ind2=" "><subfield code="2">23</subfield><subfield code="1">01</subfield><subfield code="x">0830</subfield><subfield code="a">olr-d</subfield></datafield><datafield tag="995" ind1=" " ind2=" "><subfield code="2">60</subfield><subfield code="1">01</subfield><subfield code="x">0705</subfield><subfield code="a">OLRD</subfield></datafield><datafield tag="995" ind1=" " ind2=" "><subfield code="2">63</subfield><subfield code="1">01</subfield><subfield code="x">3401</subfield><subfield code="a">ORD</subfield></datafield><datafield tag="995" ind1=" " ind2=" "><subfield code="2">132</subfield><subfield code="1">01</subfield><subfield code="x">0959</subfield><subfield code="a">OLR-DISS</subfield></datafield><datafield tag="995" ind1=" " ind2=" "><subfield code="2">151</subfield><subfield code="1">01</subfield><subfield code="x">0546</subfield><subfield code="a">OLR-ODISS</subfield></datafield><datafield tag="995" ind1=" " ind2=" "><subfield code="2">161</subfield><subfield code="1">01</subfield><subfield code="x">0960</subfield><subfield code="a">ORD</subfield></datafield><datafield tag="995" ind1=" " ind2=" "><subfield code="2">293</subfield><subfield code="1">01</subfield><subfield code="x">3293</subfield><subfield code="a">ORD</subfield></datafield><datafield tag="998" ind1=" " ind2=" "><subfield code="2">23</subfield><subfield code="1">01</subfield><subfield code="x">0830</subfield><subfield code="0">2014-05-29:01:06:56</subfield></datafield></record></collection>
|
score |
7.3996754 |