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Physical Sciences and Mathematics Commons

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Environmental Sciences

University of Vermont

Graduate College Dissertations and Theses

Decision making

Publication Year

Articles 1 - 2 of 2

Full-Text Articles in Physical Sciences and Mathematics

Governing Water Quality Limits In Agricultural Watersheds, Courtney Ryder Hammond Wagner Jan 2019

Governing Water Quality Limits In Agricultural Watersheds, Courtney Ryder Hammond Wagner

Graduate College Dissertations and Theses

The diffuse runoff of agricultural nutrients, also called agricultural nonpoint source pollution (NPS), is a widespread threat to freshwater resources. Despite decades of research into the processes of eutrophication and agricultural nutrient management, social, economic, and political barriers have slowed progress towards improving water quality. A critical challenge to managing agricultural NPS pollution is motivating landowners to act against their individual farm production incentives in response to distant ecological impacts. The complexity of governing the social-ecological system requires improved understanding of how policy shapes farmer behavior to improve the state of water quality. This dissertation contributes both theoretically and empirically …


Applying Bayesian Belief Network To Understand Public Perception On Green Stormwater Infrastructures In Vermont, Qing Ren Jan 2018

Applying Bayesian Belief Network To Understand Public Perception On Green Stormwater Infrastructures In Vermont, Qing Ren

Graduate College Dissertations and Theses

Decisions of adopting best management practices made on residential properties play an important role in reduction of nutrient loading from non-point sources into Lake Champlain and other waterbodies in Vermont. In this study, we use Bayesian belief network (BBN) to analyze a 2015 survey dataset about adoption of six types of green infrastructures (GSIs) in Vermont’s residential areas. Learning BBNs from physical probabilities of the variables provides a visually explicit approach to reveal the message delivered by the dataset. Using both unsupervised and supervised machine learning algorithms, we are able to generate networks that connect the variables of interest and …