DOAJ Open Access 2025

Inverse Bayesian Methods for Groundwater Vulnerability Assessment

Nasrin Taghavi Robert K. Niven Matthias Kramer David J. Paull

Abstrak

Groundwater vulnerability assessment (GVA) is critical for understanding contaminant migration into groundwater systems, yet conventional methods often overlook its probabilistic nature. Bayesian inference offers a robust framework using Bayes’ rule to enhance decision-making through posterior probability calculations. This study introduces inverse Bayesian methods for GVA using spatial-series data, focusing on nitrate concentrations in groundwater as an indicator of groundwater vulnerability in agricultural catchments. Using the joint maximum <i>a-posteriori</i> (JMAP) and variational Bayesian approximation (VBA) algorithms, the advantages of the Bayesian framework over traditional index-based methods are demonstrated for GVA of the Burdekin Basin, Queensland, Australia. This provides an evidence-based methodology for GVA which enables model ranking, parameter estimation, and uncertainty quantification.

Topik & Kata Kunci

Mechanical drawing. Engineering graphics Physical and theoretical chemistry

Penulis (4)

Nasrin Taghavi

Robert K. Niven

Matthias Kramer

David J. Paull

Format Sitasi

APA MLA BibTeX

Taghavi, N., Niven, R.K., Kramer, M., Paull, D.J. (2025). Inverse Bayesian Methods for Groundwater Vulnerability Assessment. https://doi.org/10.3390/psf2025012014

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Informasi Jurnal

Tahun Terbit: 2025
Sumber Database: DOAJ
DOI: 10.3390/psf2025012014
Akses: Open Access ✓