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Conceptual Bayesian Networks for Groundwater Remediation and Assessment
Citation:
Carriger, John F., Michael C. Brooks, C. Acheson, R. Herrmann, AND L. Rhea. Conceptual Bayesian Networks for Groundwater Remediation and Assessment . Presented at 2024 BayesiaLab Spring Conference, Cincinnati, OH, April 11 - 12, 2024.
Impact/Purpose:
This presentation describes a conceptual modeling approach for groundwater assessment and remediation. The basis of the conceptual models are the structures of Bayesian networks. The qualitative portion of Bayesian networks provides a means for relating the key uncertain variables of a problem in a causal fashion. These conceptual Bayesian networks are demonstrated through comparisons with a stylized groundwater remediation conceptual site model. The components of the conceptual Bayesian network such as modifiers, system variables, and interventions are demonstrated in this example. Comparisons are made with traditional pictorial conceptual site models. The application of the conceptual Bayesian network structure with remediation interventions and with lines of evidence for the presence and magnitude of contaminants are demonstrated. The usage of conceptual models with Bayesian networks may provide informative modeling tools that provide the basis for quantitative models, weight of evidence assessments, and communication of site knowledge throughout all phases of a risk assessment and management process.
Description:
Causal structural models are used to capture knowledge of a problem domain through framing potential events as random variables and connections as causal arcs. Moreover, the properties of causal structural models foster additional insights on causal and inferential interactions among variables from interventions and observations. Conceptual site models are commonly used in environmental assessments for capturing the knowledge of the fate, transport, and risks at contaminated sites and form the basis for simulation models. The usage of causal structural models with conceptual site modeling may provide additional value for site remediation and assessments. We call this combination conceptual Bayesian networks (CBNs) and explore their application potential in contaminated site management for assessing the subsurface movement of contaminated plumes. Once constructed, the CBN can capture the hypothesized locations and movements of a plume as well as critical zones of offsite flux. Causal pathway identification can examine offsite transport pathways and the potential effects of remediation decisions that intervene on those pathways. Interventions for containing or removing subsurface contamination and breaking the transport pathways are graphically represented as decision nodes. Finally, measurement node types can explicitly include lines of evidence for subsurface processes in the CBN. Acausal pathways from influence paths provide additional information on statistical inferences when lines of evidence are observed individually or in conjunction. The CBN concept may provide additional insights beyond traditional conceptual site models and could be a valuable component in a site manager's toolbox. John Carriger is a research scientist at the U.S. Environmental Protection Agency's Office of Research and Development in Cincinnati, Ohio. John has a marine science Ph.D. from the College of William and Mary. John's research interests include applying risk assessment, decision analysis, and weight of evidence tools to environmental problems. The views expressed in this presentation are those of the authors and do not necessarily represent the views or policies of the U.S. Environmental Protection Agency.