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 conduct inference to look into the probabilistic associations between the variables. Unsupervised learning reveals the underlying structures of the dataset without presumptions. Supervised learning provides insights for how each factor (e.g. demographics, risk perception, and attribution of responsibilities) influence individuals’ pro-environmental behaviors. We also compare the effectiveness of BBN approach and logistic regression in predicting the pro-environmental behaviors (adoption of GSIs).
The results show that influencing factors for current adoption vary by different types of GSI. Risk perception of stormwater issues are associated with adoption of GSIs. Runoff issues are more likely to be considered as the governments’ (town, state, and federal agencies) responsibility, whereas lawn erosion is more likely to be considered as the residents’ own responsibility. When using the same set of variables to predict pro-environmental behaviors (adoption of GSI), BBN approach produces more accurate prediction compared to logistic regression.
| Identifer | oai:union.ndltd.org:uvm.edu/oai:scholarworks.uvm.edu:graddis-1834 |
| Date | 01 January 2018 |
| Creators | REN, Qing |
| Publisher | ScholarWorks @ UVM |
| Source Sets | University of Vermont |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Graduate College Dissertations and Theses |
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