Pooling Strength Amongst Limited Datasets using Hierarchical Bayesian Analysis, with Application to Pyroclastic Density Current Mobility Metrics

Authors

Sarah E. Ogburn, State University of New York at BuffaloFollow
James Berger, Duke University
Eliza S. Calder, University of EdinburghFollow
Danilo Lopes, Federal University of Sao Carlos
Abani K. Patra, State University of New York at BuffaloFollow
E. Bruce Pitman, State University of New York at Buffalo
Regis Rutarindwa, Marquette University
Elaine Spiller, Marquette UniversityFollow
Robert L. Wolpert, Duke University

Document Type

Article

Language

eng

Format of Original

26 p.

Publication Date

4-20-2016

Publisher

IAVCEI Commission on Statistics in Volcanology (COSIV)

Source Publication

Statistics in Volcanology

Source ISSN

2163-338X

Original Item ID

DOI: 10.5038/2163-338X.2.1

Abstract

In volcanology, the sparsity of datasets for individual volcanoes is an important problem, which, in many cases, compromises our ability to make robust judgments about future volcanic hazards. In this contribution we develop a method for using hierarchical Bayesian analysis of global datasets to combine information across different volcanoes and to thereby improve our knowledge at individual volcanoes. The method is applied to the assessment of mobility metrics for pyroclastic density currents in order to better constrain input parameters and their related uncertainties for forward modeling. Mitigation of risk associated with such flows depends upon accurate forecasting of possible inundation areas, often using empirical models that rely on mobility metrics measured from the deposits of past flows, or on the application of computational models, several of which take mobility metrics, either directly or indirectly, as input parameters. We use hierarchical Bayesian modeling to leverage the global record of mobility metrics from the FlowDat database, leading to considerable improvement in the assessment of flow mobility where the data for a particular volcano is sparse. We estimate the uncertainties involved and demonstrate how they are improved through this approach. The method has broad applicability across other areas of volcanology where relationships established from broader datasets can be used to better constrain more specific, sparser, datasets. Employing such methods allows us to use, rather than shy away from, limited datasets, and allows for transparency with regard to uncertainties, enabling more accountable decision-making.

Comments

Published version. Statistics in Volcanology, Vol. 2 (April 20, 2016): 1-26. DOI. © 2016 The Authors(s). Used with permission.

Creative Commons License

This work is licensed under a Creative Commons Attribution-Noncommercial 3.0 License

Recommended Citation

Ogburn, Sarah E.; Berger, James; Calder, Eliza S.; Lopes, Danilo; Patra, Abani K.; Pitman, E. Bruce; Rutarindwa, Regis; Spiller, Elaine; and Wolpert, Robert L., "Pooling Strength Amongst Limited Datasets using Hierarchical Bayesian Analysis, with Application to Pyroclastic Density Current Mobility Metrics" (2016). Mathematics, Statistics and Computer Science Faculty Research and Publications. 483.
https://epublications.marquette.edu/mscs_fac/483