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A Bayesian Probability Network Approach To Predictive Modeling in Support of Environmental Decision Making
EPA Grant Number: U915590Title: A Bayesian Probability Network Approach To Predictive Modeling in Support of Environmental Decision Making
Investigators: Borsuk, Mark E.
Institution: Duke University
EPA Project Officer: Edwards, Jason
Project Period: August 1, 1999 through August 1, 2002
Project Amount: $102,000
RFA: STAR Graduate Fellowships (1999)
Research Category: Academic Fellowships , Fellowship - Environmental Decision Making , Economics and Decision Sciences
Description:
Objective:The objective of this research project is to demonstrate that probability network models represent an improved approach to predictive modeling used for environmental management.
Approach:A probability network model is being developed and applied to the problem of eutrophication in the Neuse River, North Carolina. Also called a Bayesian probability network, or a ?Bayes net,? this model consists of the set of variables of interest in the system being modeled as well as a set of assertions concerning the probabilistic relationships among the variables. These relationships are quantified using historical data, models, and expert judgment. Probabilistic predictions of model endpoints are then made that are based on the entire set of conditional probabilities that have been assessed for each system variable. Not only does this network structure provide a more integrated approach to uncertainty analysis, but it also allows easy updating of prediction and inference when observations of model variables are made. This capability is particularly important when applied to a natural system in which additional monitoring is likely to occur concurrent with the modeling effort.
Expected Results:This study will determine if probability network models are more effective than predictive modeling for environmental management. The probability network method could remedy many of the traditional shortcomings of science used for decisionmaking.
Supplemental Keywords:integrated modeling, probabilistic prediction, stakeholder involvement, publicly meaningful endpoints, water quality modeling, Bayesian inference. , Economic, Social, & Behavioral Science Research Program, Scientific Discipline, RFA, Social Science, decision-making, Environmental Statistics, Economics & Decision Making, stakeholder, decision analysis, decision making, Bayesian method, probability network models, integrated modeling, water quality model, Bayesian approach