A hierarchical Bayesian model averaging framework for groundwater prediction under uncertainty

Authors

Nima Chitsazan, Department of Civil and Environmental Engineering, Louisiana State University, 3418G Patrick F. Taylor Hall, Baton Rouge, LA 70803.
Frank T-C Tsai, Biodesign Center for Environmental Health Engineering, Biodesign Institute, Arizona State University, Tempe, AZ 85287, USA.
Devrim Kaya, School of Chemical, Biological, and Environmental Engineering, Oregon State University (OSU), Corvallis, OR, USA.
Md Aminul Islam, COVID-19 Diagnostic Laboratory, Department of Microbiology, Noakhali Science and Technology University, Noakhali, 3814, Bangladesh; Advanced Molecular Laboratory, Department of Microbiology, President Abdul Hamid Medical College, Karimganj, Kishoreganj, Bangladesh.
Bikash Malla, Interdisciplinary Center for River Basin Environment, University of Yamanashi, 4-3-11 Takeda, Kofu, Yamanashi 400-8511, Japan.
Samendra P. Sherchan, Department of Biology, Morgan State University, Baltimore, MD, USA; Department of Environmental Health Sciences, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, USA.
Ahmad I. Al-Mustapha, Department of Food Hygiene and Environmental Health, Faculty of Veterinary Medicine, University of Helsinki, Finland; Department of Veterinary Public Health and Preventive Medicine, Faculty of Veterinary Medicine, University of Ibadan, Oyo State, Nigeria; Department of Veterinary Services, Kwara State Ministry of Agriculture and Rural Development, Ilorin, Kwara State, Nigeria.

Document Type

Article

Publication Date

1-1-2015

Abstract

Groundwater prediction models are subjected to various sources of uncertainty. This study introduces a hierarchical Bayesian model averaging (HBMA) method to segregate and prioritize sources of uncertainty in a hierarchical structure and conduct BMA for concentration prediction. A BMA tree of models is developed to understand the impact of individual sources of uncertainty and uncertainty propagation to model predictions. HBMA evaluates the relative importance of different modeling propositions at each level in the BMA tree of model weights. The HBMA method is applied to chloride concentration prediction for the "1,500-foot" sand of the Baton Rouge area, Louisiana from 2005 to 2029. The groundwater head data from 1990 to 2004 is used for model calibration. Four sources of uncertainty are considered and resulted in 180 flow and transport models for concentration prediction. The results show that prediction variances of concentration from uncertain model elements are much higher than the prediction variance from uncertain model parameters. The HBMA method is able to quantify the contributions of individual sources of uncertainty to the total uncertainty.

Publication Source (Journal or Book title)

Ground water

First Page

305

Last Page

16

Recommended Citation

Chitsazan, N., Tsai, F. T., Kaya, D., Islam, M. A., Malla, B., Sherchan, S. P., & Al-Mustapha, A. I. (2015). A hierarchical Bayesian model averaging framework for groundwater prediction under uncertainty. Ground water, 53 (2), 305-16. https://doi.org/10.1111/gwat.12207