• Skip to main content
  • Skip to after header navigation
  • Skip to site footer
ERN: Emerging Researchers National Conference in STEM

ERN: Emerging Researchers National Conference in STEM

  • About
    • About AAAS
    • About the NSF
    • About the Conference
    • Partners/Supporters
    • Project Team
  • Conference
  • Abstracts
    • Undergraduate Abstract Locator
    • Graduate Abstract Locator
    • Abstract Submission Process
    • Presentation Schedules
    • Abstract Submission Guidelines
    • Presentation Guidelines
  • Travel Awards
  • Resources
    • Award Winners
    • Code of Conduct-AAAS Meetings
    • Code of Conduct-ERN Conference
    • Conference Agenda
    • Conference Materials
    • Conference Program Books
    • ERN Photo Galleries
    • Events | Opportunities
    • Exhibitor Info
    • HBCU-UP/CREST PI/PD Meeting
    • In the News
    • NSF Harassment Policy
    • Plenary Session Videos
    • Professional Development
    • Science Careers Handbook
    • Additional Resources
    • Archives
  • Engage
    • Webinars
    • ERN 10-Year Anniversary Videos
    • Plenary Session Videos
  • Contact Us
  • Login

Convergence Diagnostics of MCMCs for Reliable Prediction of Contaminant Transport

Faculty #41
Discipline: Mathematics & Statistics
Subcategory: STEM Research

Arunasalam Rahunanthan - Central State University
Co-Author(s): Abdullah Al-Mamun, University of Texas at Dallas; Felipe Pereira, University of Texas at Dallas



We consider the prediction of contaminant in a water aquifer. In order to predict the contaminant concentration in time, we first characterize the subsurface properties of the aquifer, such as permeability, by using very limited data in the form of fractional flow curves in monitoring wells of the aquifer. A Bayesian statistical framework is used for reconstructing the permeability distribution of the aquifer. In the framework we run several parallel Markov Chain Monte Carlo (MCMC) simulations. In this approach, we need to determine when it is safe to stop the MCMC simulations for a reliable characterization of the permeability field. There are several convergence diagnostics available for this purpose and those diagnostics fall into two categories: the first category of diagnostics entirely depends on the output values of the MCMC simulation and those in the second category use not only the output values but also the information on the target distribution. In the first category, Brooks and Gelman [1] proposed a convergence diagnostic that uses Multivariate Potential Scale Reduction Factor (MPSRF) to decide when to terminate MCMC simulations. In this poster presentation, we first propose a fitting procedure for the MPRSF data that allows us to estimate the number of iterations for the convergence. Then we present an analysis of ensembles of fractional flow curves suggesting that the number of iterations required for convergence through the MPSRF analysis is excessive. Also, the analysis, which is our proposed convergence diagnostic, provides a criterion to stop MCMC simulations for a reliable prediction of the contaminant in the aquifer. The prediction results indicate that the proposed convergence diagnostic is very reliable in our application. Reference: [1] A. Gelman and S. Brooks, General methods for monitoring convergence of iterative simulations, Journal of Computational and Graphical Statistics, vol. 7, pp. 434–455, 1998.

Funder Acknowledgement(s): HRD-1600818

Faculty Advisor: None Listed,
NSF Affiliation: HBCU-UP

Sidebar

Abstract Locators

  • Undergraduate Abstract Locator
  • Graduate Abstract Locator

This material is based upon work supported by the National Science Foundation (NSF) under Grant No. DUE-1930047. Any opinions, findings, interpretations, conclusions or recommendations expressed in this material are those of its authors and do not represent the views of the AAAS Board of Directors, the Council of AAAS, AAAS’ membership or the National Science Foundation.

AAAS

1200 New York Ave, NW
Washington,DC 20005
202-326-6400
Contact Us
About Us

  • LinkedIn
  • Facebook
  • Instagram
  • Twitter
  • YouTube

The World’s Largest General Scientific Society

Useful Links

  • Membership
  • Careers at AAAS
  • Privacy Policy
  • Terms of Use

Focus Areas

  • Science Education
  • Science Diplomacy
  • Public Engagement
  • Careers in STEM

Focus Areas

  • Shaping Science Policy
  • Advocacy for Evidence
  • R&D Budget Analysis
  • Human Rights, Ethics & Law

© 2023 American Association for the Advancement of Science