Discussing Issues in Uncertainty Quantification. The Case of Geohazard Assessments

Authors

Keywords:

Uncertainty quantification, Geohazard assessments, Parameters, Prediction credibility, Input quantities, Assumptions

Synopsis

By making explicit the modeling choices and assumptions made, we analyze some issues in quantifying uncertainty using geohazard models. Under the often condition of very limited data, a major problem is constraining the many parameters involved. We conclude that, despite the availability of recently developed sophistications, the quantification based on these ideal parameterized models can hardly be justified since, e.g., they will only reflect some aspects of the uncertainty involved. This calls for more insightful approaches which are yet to be developed.

This abstract is part of: Book of Extended Abstracts for the 32nd European Safety and Reliability Conference". Edited by Maria Chiara Leva, Edoardo Patelli, Luca Podofillini, and Simon Wilson. ESREL2022 Organizers. Published by Research Publishing, Singapore. https://www.esrel2022.com/

Author Biography

Ibsen Chivata Cardenas

Postdoctoral fellow
Faculty of Science and Technology
Department of Safety, Economics and Planning
University of Stavanger
ibsen.chivatacardenas@uis.no

References

Albert, C.G., U., Callies, and U. von Toussaint (2022). A Bayesian approach to the estimation of parameters and their interdependencies in environmental modeling. Entropy 24(2), 231-262.

https://doi.org/10.3390/e24020231

Betz, W. (2017). Bayesian inference of engineering models. Technische Universitat Munchen.

Lu, P., and P.F. Lermusiaux (2021). Bayesian leaming of stochastic dynamical models. Physica D: Nonlinear Phenomena 427, 133003.

https://doi.org/10.1016/j.physd.2021.133003

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Published

September 19, 2022

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This work is licensed under a Creative Commons Attribution 4.0 International License.