Author
Title
A Bayesian Network Approach of Uncertainty Quantification for Groundwater Reactive Transport Modeling
Abstract
Bayesian networks (BNs), also known as belief networks, belong to the family of probabilistic graphical models (GMs). A Bayesian network consists of a graphical structure and a probabilistic description of the relationships among different variables of the system analyzed. The graphical structure explicitly represents cause and effect relationships that allow a complex causal chain linking actions to be factored into an articulated series of conditional relationships. Due to these characteristics, Bayesian networks can be particularly useful for uncertainty quantification of complex groundwater reactive transport models with multiple components related by different dependencies.