scholarly journals Modeling the Influence of Public Risk Perceptions on the Adoption of Green Stormwater Infrastructure: An Application of Bayesian Belief Networks Versus Logistic Regressions on a Statewide Survey of Households in Vermont

Water ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2793
Author(s):  
Qing Ren ◽  
Asim Zia ◽  
Donna M. Rizzo ◽  
Nancy Mathews
Keyword(s):  
Logistic Regression ◽  
Green Infrastructure ◽  
Risk Perceptions ◽  
Bayesian Belief Networks ◽  
Point Sources ◽  
Supervised Machine Learning ◽  
Belief Networks ◽  
Rain Gardens ◽  
Environmental Behaviors ◽  
Public Risk

There is growing environmental psychology and behavior literature with mixed empirical evidence about the influence of public risk perceptions on the adoption of environmentally friendly “green behaviors”. Adoption of stormwater green infrastructure on residential properties, while costlier in the short term compared to conventional greywater infrastructure, plays an important role in the reduction of nutrient loading from non-point sources into freshwater rivers and lakes. In this study, we use Bayesian Belief Networks (BBNs) to analyze a 2015 survey dataset (sample size = 472 respondents) about the adoption of green infrastructure (GSI) in Vermont’s residential areas, most of which are located in either the Lake Champlain Basin or Connecticut River Basin. Eight categories of GSI were investigated: roof diversion, permeable pavement, infiltration trenches, green roofs, rain gardens, constructed wetlands, tree boxes, and others. Using both unsupervised and supervised machine learning algorithms, we used Bayesian Belief Networks to quantify the influence of public risk perceptions on GSI adoption while accounting for a range of demographic and spatial variables. We also compare the effectiveness of the Bayesian Belief Network approach and logistic regression in predicting the pro-environmental behaviors (adoption of GSI). The results show that influencing factors for current adoption differ by the type of GSI. Increased perception of risk from stormwater issues is associated with the adoption of rain gardens and infiltration trenches. Runoff issues are more likely to be considered the governments’ (town, state, and federal agencies) responsibility, whereas lawn erosion is more likely to be considered the residents’ responsibility. When using the same set of variables to predict pro-environmental behaviors (adoption of GSI), the BBN approach produces more accurate predictions compared to logistic regression. The results provide insights for further research on how to encourage residents to take measures for mitigating stormwater issues and stormwater management.

Using Bayesian network models to incorporate uncertainty in the economic analysis of pollution abatement measures under the water framework directive

2005 ◽  
Vol 5 (6) ◽  
pp. 95-104 ◽  
Author(s):  
D.N. Barton ◽  
T. Saloranta ◽  
T.H. Bakken ◽  
A. Lyche Solheim ◽  
J. Moe ◽  
...  
Keyword(s):  
Water Quality ◽  
At Risk ◽  
Economic Valuation ◽  
Pollution Abatement ◽  
Network Models ◽  
Cost Effective ◽  
Bayesian Belief Networks ◽  
Probabilistic Interpretation ◽  
Belief Networks ◽  
Benefit Cost Analysis

The evaluation of water bodies “at risk” of not achieving the Water Framework Directive's (WFD) goal of “good status” begs the question of how big a risk is acceptable before a programme of measures should be implemented. Documentation of expert judgement and statistical uncertainty in pollution budgets and water quality modelling, combined with Monte Carlo simulation and Bayesian belief networks, make it possible to give a probabilistic interpretation of “at risk”. Combined with information on abatement costs, a cost-effective ranking of measures based on expected costs and effect can be undertaken. Combined with economic valuation of water quality, the definition of “disproportionate cost” of abatement measures compared to benefits of achieving “good status” can also be given a probabilistic interpretation. Explicit modelling of uncertainty helps visualize where research and consulting efforts are most critical for reducing uncertainty. Based on data from the Morsa catchment in South-Eastern Norway, this paper discusses the relative merits of using Bayesian belief networks when integrating biophysical modelling results in the benefit-cost analysis of derogations and cost-effectiveness ranking of abatement measures under the WFD.

scholarly journals Using Bayesian Belief Networks to assess the influence of landscape connectivity on ecosystem service trade-offs and synergies in urban landscapes in the UK

2021 ◽  
Author(s):  
James D. Karimi ◽  
Jim A. Harris ◽  
Ron Corstanje
Keyword(s):  
Functional Connectivity ◽  
Ecosystem Service ◽  
Urban Areas ◽  
Landscape Connectivity ◽  
Structural Characteristics ◽  
Bird Species ◽  
Bayesian Belief Networks ◽  
Belief Networks ◽  
Patch Area ◽  
Trade Offs

Abstract Context Landscape connectivity is assumed to influence ecosystem service (ES) trade-offs and synergies. However, empirical studies of the effect of landscape connectivity on ES trade-offs and synergies are limited, especially in urban areas where the interactions between patterns and processes are complex. Objectives The objectives of this study were to use a Bayesian Belief Network approach to (1) assess whether functional connectivity drives ES trade-offs and synergies in urban areas and (2) assess the influence of connectivity on the supply of ESs. Methods We used circuit theory to model urban bird flow of P. major and C. caeruleus at a 2 m spatial resolution in Bedford, Luton and Milton Keynes, UK, and Bayesian Belief Networks (BBNs) to assess the sensitivity of ES trade-offs and synergies model outputs to landscape and patch structural characteristics (patch area, connectivity and bird species abundance). Results We found that functional connectivity was the most influential variable in determining two of three ES trade-offs and synergies. Patch area and connectivity exerted a strong influence on ES trade-offs and synergies. Low patch area and low to moderately low connectivity were associated with high levels of ES trade-offs and synergies. Conclusions This study demonstrates that landscape connectivity is an influential determinant of ES trade-offs and synergies and supports the conviction that larger and better-connected habitat patches increase ES provision. A BBN approach is proposed as a feasible method of ES trade-off and synergy prediction in complex landscapes. Our findings can prove to be informative for urban ES management.

scholarly journals Supervised machine learning and logistic regression identifies novel epistatic risk factors with PTPN22 for rheumatoid arthritis

2010 ◽  
Vol 11 (3) ◽  
pp. 199-208 ◽  
Author(s):  
F B S Briggs ◽  
P P Ramsay ◽  
E Madden ◽  
J M Norris ◽  
V M Holers ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Machine Learning ◽  
Risk Factors ◽  
Logistic Regression ◽  
Supervised Machine Learning

Data-Driven Dynamic Probabilistic Reserve Sizing Based on Dynamic Bayesian Belief Networks

2019 ◽  
Vol 34 (3) ◽  
pp. 2281-2291 ◽  
Author(s):  
Fateme Fahiman ◽  
Steven Disano ◽  
Sarah Monazam Erfani ◽  
Pierluigi Mancarella ◽  
Christopher Leckie
Keyword(s):  
Bayesian Belief Networks ◽  
Data Driven ◽  
Belief Networks

scholarly journals Perceptions of Health Risks from Hot Weather, and Coping Behaviors among Ethnic Minority Groups in Mountain Areas of China: A Case Study in the Tujia and Miao Autonomous Prefecture

2018 ◽  
Vol 15 (11) ◽  
pp. 2498 ◽  
Author(s):  
Hui Ye ◽  
Juan Ma ◽  
Yang Wu ◽  
Ying Zhang
Keyword(s):  
Logistic Regression ◽  
Risk Perceptions ◽  
Regression Models ◽  
Minority Groups ◽  
Coping Behaviors ◽  
Ethnic Minority Groups ◽  
Mountain Areas ◽  
Logistic Regression Models ◽  
Hot Weather ◽  
And Coping

Limited research focuses on risk perceptions of hot weather among ethnic minority groups in remote mountain areas of China. Adopting a multi-stage sampling method, this study received completed questionnaires from 643 participates in Enshi Tujia and Miao Autonomous Prefecture of China in 2017 and 2018. We used multivariate logistic regression models to explore the factors affecting risk perceptions and coping behaviors with regards to hot weather. Results showed that despite a relatively high level of risk perception, the study population in the mountain areas of China had a very low level of preparedness in responding to the risks from heat, and a lack of professional health knowledge in general. In particular, 61.3% (95% CI: 57.1%−5.6%) of the participants felt increasing temperatures in recent years, 73.2% (95% CI: 69.3%−7.0%) thought extreme high temperatures would be a health threat, and 61.3% (95% CI: 57.1%−5.4%) reported physical discomfort during hot weather. However, only 12% (95% CI: 9.5%−4.5%) had the information or knowledge to stay healthy during the extreme high temperatures, and only 24.2% had (95% CI: 20.8%−7.6%) preparation. The logistic regression models suggested that ethnic group, health status, marital status, gender, and employment could affect their perceptions, which could significantly affect the adoption of coping behaviors. In conclusion, our findings have significant implications for developing policies and health education and promotion programs for ethnic minorities in remote regions to maintain good health during hot weather.

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