Dr. Kristin Vogel

Kontakt

Universität Potsdam,
Institut für Erd- und Umweltwissenschaften

Dr. Kristin Vogel
Haus 1, Raum 1.22

Karl-Liebknecht-Str. 24-25
14476 Potsdam-Golm
E-Mail:
kvog@geo.uni-potsdam.de
Telefon:
+49 331 977 6257
Fax:
+49 331 977 5700
Dr. Kristin Vogel

 

  • Biografie
  • Forschung
  • Lehre
  • Publikationen

Biografie

seit 2010
akademische Mitarbeiterin am Inst. f. Erd- und Umweltwissenschaften, Universität Potsdam
2014
Doctor rer. nat. in Allgemeine Geophysik, Universität Potsdam
2009 - 2010
Mitarbeiterin bei DTU Aqua - National Institute of Aquatic Resources, Denmark
2009
Diplom in Mathematik, Universität Potsdam
2007
Praktikantin bei Tel-Tek (dept. POSTEC), Norway

Forschung

Considering natural hazards and risks, we are permanently confronted with issues of uncertainty, rising questions as "How can we determine and capture different kinds of uncertainty in our models?" or "How can we communicate uncertainty to decision makers, politicians and public?" 

My interest in the application and adoption of methods from (Bayesian) statistics and the field of machine learning (i.e. graphical networks such as Bayesian networks) for probabilistic natural hazard assessments. 

Lehre

MGEW23 Quantitative Grundlagen der Analyse von Naturgefahren

MGPW12 Einführung in Bayessche Netze für Geowissenschaftler

Publikationen

Vogel, K., J. Laudan, T. Sieg, V. Rözer, B. Winter, A.H. Thieken (2017):
Data collection for a damage assessment after the flash flood in Braunsbach
(Germany) in May 2016. GFZ Data Services. DOI: 10.5880/fidgeo.2017.015

Laudan, J., V. Rözer, T. Sieg, K. Vogel, & A.H. Thieken (2017): Dam-
age assessment in Braunsbach 2016: data collection and analysis for an
improved understanding of damaging processes during flash floods. Natural
Hazards and Earth System Sciences, 17(12),2163-2179. DOI: 10.5194/nhess-17-2163-2017

Vogel, K. (2017): Learning Markov Blankets - Extending on a MAP
Criterion for Bayesian Network Learning. In Safety, Reliability, Risk, Re-
silience and Sustainability of Structures and Infrastructure, 12th Interna-
tional Conference on Structural Safety & Reliability, Vienna, Austria, 6-10
August 2017, 2868-2877.

Sieg, T., K. Vogel, B. Merz, and H. Kreibich (2017), Tree-based flood damage modeling of companies: Damage processes and model performance. Water Resour. Res., 53, 6050–6068, DOI: 10.1002/2017WR020784

Vogel, K., U. Ozturk, A. Riemer, J. Laudan, T. Sieg, D. Wendi, A. Agarwal, V. Rözer, O. Korup, A. Thieken, 2017. Die Sturzflut von Braunsbach am 29. Mai 2016 - Entstehung, Ablauf und Schäden eines “Jahrhundertereignisses”. Teil 2: Geomorphologische Prozesse und Schadensanalyse. Hydrologie & Wasserbewirtschaftung, 61(3), 163-175, DOI: 10.5675/HyWa_2017,3_2

Vogel, K., Riggelsen, C., Korup, O., Scherbaum, F., 2014. Bayesian network learning for natural hazard analyses, Natural Hazards and Earth System Science, 14(9), 2605-2626, DOI: 10.5194/nhess-14-2605-2014

Schröter, K., Kreibich, H., Vogel, K., Riggelsen, C., Scherbaum, F., Merz, B., 2014. How useful are complex flood damage models?, Water Resources Research, 50(4), 3378–3395, DOI: 10.1002/2013WR014396

Vogel, K., 2014. Applications of Bayesian networks in natural hazard assessmentsDoctoral dissertation, Universitätsbibliothek

Vogel, K., Riggelsen, C., Scherbaum, F., Schroeter, K., Kreibich, H., Merz, B., 2013. Challenges for Bayesian Network Learning in a Flood Damage Assessment Application. In Proceedings of the 11th International Conference on Structural Safety & Reliability, New York, NY, USA, ICOSSAR'13

Vogel, K., Riggelsen, C., Merz, B., Kreibich, H., and F. Scherbaum, 2012. Flood Damage and Influencing Factors: A Bayesian Network Perspective. Conf. on Probabilistic Graphical Models, Granada, Spain, PGM'12, ISBN: 978-84-15536-57-4, pp. 347-354.

Vogel, K., Riggelsen, C., Kühn, N., Scherbaum, F., 2012. Graphical Models as Surrogates for Complex Ground Motion Models. 11th International Conference on Artificial Intelligence and Soft Computing, ICAISC'12, ISBN 978-3-642-29346-7, pp. 188-195.

Biografie

seit 2010
akademische Mitarbeiterin am Inst. f. Erd- und Umweltwissenschaften, Universität Potsdam
2014
Doctor rer. nat. in Allgemeine Geophysik, Universität Potsdam
2009 - 2010
Mitarbeiterin bei DTU Aqua - National Institute of Aquatic Resources, Denmark
2009
Diplom in Mathematik, Universität Potsdam
2007
Praktikantin bei Tel-Tek (dept. POSTEC), Norway

Forschung

Considering natural hazards and risks, we are permanently confronted with issues of uncertainty, rising questions as "How can we determine and capture different kinds of uncertainty in our models?" or "How can we communicate uncertainty to decision makers, politicians and public?" 

My interest in the application and adoption of methods from (Bayesian) statistics and the field of machine learning (i.e. graphical networks such as Bayesian networks) for probabilistic natural hazard assessments. 

Lehre

MGEW23 Quantitative Grundlagen der Analyse von Naturgefahren

MGPW12 Einführung in Bayessche Netze für Geowissenschaftler

Publikationen

Vogel, K., J. Laudan, T. Sieg, V. Rözer, B. Winter, A.H. Thieken (2017):
Data collection for a damage assessment after the flash flood in Braunsbach
(Germany) in May 2016. GFZ Data Services. DOI: 10.5880/fidgeo.2017.015

Laudan, J., V. Rözer, T. Sieg, K. Vogel, & A.H. Thieken (2017): Dam-
age assessment in Braunsbach 2016: data collection and analysis for an
improved understanding of damaging processes during flash floods. Natural
Hazards and Earth System Sciences, 17(12),2163-2179. DOI: 10.5194/nhess-17-2163-2017

Vogel, K. (2017): Learning Markov Blankets - Extending on a MAP
Criterion for Bayesian Network Learning. In Safety, Reliability, Risk, Re-
silience and Sustainability of Structures and Infrastructure, 12th Interna-
tional Conference on Structural Safety & Reliability, Vienna, Austria, 6-10
August 2017, 2868-2877.

Sieg, T., K. Vogel, B. Merz, and H. Kreibich (2017), Tree-based flood damage modeling of companies: Damage processes and model performance. Water Resour. Res., 53, 6050–6068, DOI: 10.1002/2017WR020784

Vogel, K., U. Ozturk, A. Riemer, J. Laudan, T. Sieg, D. Wendi, A. Agarwal, V. Rözer, O. Korup, A. Thieken, 2017. Die Sturzflut von Braunsbach am 29. Mai 2016 - Entstehung, Ablauf und Schäden eines “Jahrhundertereignisses”. Teil 2: Geomorphologische Prozesse und Schadensanalyse. Hydrologie & Wasserbewirtschaftung, 61(3), 163-175, DOI: 10.5675/HyWa_2017,3_2

Vogel, K., Riggelsen, C., Korup, O., Scherbaum, F., 2014. Bayesian network learning for natural hazard analyses, Natural Hazards and Earth System Science, 14(9), 2605-2626, DOI: 10.5194/nhess-14-2605-2014

Schröter, K., Kreibich, H., Vogel, K., Riggelsen, C., Scherbaum, F., Merz, B., 2014. How useful are complex flood damage models?, Water Resources Research, 50(4), 3378–3395, DOI: 10.1002/2013WR014396

Vogel, K., 2014. Applications of Bayesian networks in natural hazard assessmentsDoctoral dissertation, Universitätsbibliothek

Vogel, K., Riggelsen, C., Scherbaum, F., Schroeter, K., Kreibich, H., Merz, B., 2013. Challenges for Bayesian Network Learning in a Flood Damage Assessment Application. In Proceedings of the 11th International Conference on Structural Safety & Reliability, New York, NY, USA, ICOSSAR'13

Vogel, K., Riggelsen, C., Merz, B., Kreibich, H., and F. Scherbaum, 2012. Flood Damage and Influencing Factors: A Bayesian Network Perspective. Conf. on Probabilistic Graphical Models, Granada, Spain, PGM'12, ISBN: 978-84-15536-57-4, pp. 347-354.

Vogel, K., Riggelsen, C., Kühn, N., Scherbaum, F., 2012. Graphical Models as Surrogates for Complex Ground Motion Models. 11th International Conference on Artificial Intelligence and Soft Computing, ICAISC'12, ISBN 978-3-642-29346-7, pp. 188-195.