Abstract
A prognosis model has been developed for solid waste generation from households in Hoi An City, a famous tourist city in Viet Nam. Waste sampling, followed by a questionnaire survey, was carried out to gather data. The Bayesian model average method was used to identify factors significantly associated with waste generation. Multivariate linear regression analysis was then applied to evaluate the impacts of significant factors on household waste production. The model obtained from this study indicated that household location, household size, house area per person, and family economic activity are important determinants of the waste generation rate. The models could explain about 34% of the variation of the per capita daily waste generation rate. Diagnostic tests and model validation results showed that the regression model could provide reliable results of estimated household waste. The study revealed that per capita urban household waste generation is 70-80% higher compared to a rural household. The models also showed that if a family ran a business from home, the household waste generation rate would increase by about 35%. This result provides reliable information for better waste collection and management planning. Two other significant variables (family size and house area per capita) do not contribute much (less than 20%) to waste generation. Variables accounting for household income, presence of a garden, number of rooms in a house, and percentage of members of different ages were proven to be not significant. The study provides a reliable method for estimating household waste generation, providing decision makers useful information for waste management policy development.
| Original language | English |
|---|---|
| Pages (from-to) | 385-402 |
| Number of pages | 18 |
| Journal | Global Journal of Environmental Science and Management |
| Volume | 3 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - Sep 1 2017 |
Fingerprint
Keywords
- Bayesian model average (BMA)
- Multivariate linear regression
- Municipal solid waste management (MSWM)
- Prognosis model
- Waste generation
ASJC Scopus subject areas
- Environmental Science(all)
Cite this
Predicting waste generation using Bayesian model averaging. / Hoang, M. G.; Fujiwara, Takeshi; Pham Phu, S. T.; Nguyen Thi, K. T.
In: Global Journal of Environmental Science and Management, Vol. 3, No. 4, 01.09.2017, p. 385-402.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Predicting waste generation using Bayesian model averaging
AU - Hoang, M. G.
AU - Fujiwara, Takeshi
AU - Pham Phu, S. T.
AU - Nguyen Thi, K. T.
PY - 2017/9/1
Y1 - 2017/9/1
N2 - A prognosis model has been developed for solid waste generation from households in Hoi An City, a famous tourist city in Viet Nam. Waste sampling, followed by a questionnaire survey, was carried out to gather data. The Bayesian model average method was used to identify factors significantly associated with waste generation. Multivariate linear regression analysis was then applied to evaluate the impacts of significant factors on household waste production. The model obtained from this study indicated that household location, household size, house area per person, and family economic activity are important determinants of the waste generation rate. The models could explain about 34% of the variation of the per capita daily waste generation rate. Diagnostic tests and model validation results showed that the regression model could provide reliable results of estimated household waste. The study revealed that per capita urban household waste generation is 70-80% higher compared to a rural household. The models also showed that if a family ran a business from home, the household waste generation rate would increase by about 35%. This result provides reliable information for better waste collection and management planning. Two other significant variables (family size and house area per capita) do not contribute much (less than 20%) to waste generation. Variables accounting for household income, presence of a garden, number of rooms in a house, and percentage of members of different ages were proven to be not significant. The study provides a reliable method for estimating household waste generation, providing decision makers useful information for waste management policy development.
AB - A prognosis model has been developed for solid waste generation from households in Hoi An City, a famous tourist city in Viet Nam. Waste sampling, followed by a questionnaire survey, was carried out to gather data. The Bayesian model average method was used to identify factors significantly associated with waste generation. Multivariate linear regression analysis was then applied to evaluate the impacts of significant factors on household waste production. The model obtained from this study indicated that household location, household size, house area per person, and family economic activity are important determinants of the waste generation rate. The models could explain about 34% of the variation of the per capita daily waste generation rate. Diagnostic tests and model validation results showed that the regression model could provide reliable results of estimated household waste. The study revealed that per capita urban household waste generation is 70-80% higher compared to a rural household. The models also showed that if a family ran a business from home, the household waste generation rate would increase by about 35%. This result provides reliable information for better waste collection and management planning. Two other significant variables (family size and house area per capita) do not contribute much (less than 20%) to waste generation. Variables accounting for household income, presence of a garden, number of rooms in a house, and percentage of members of different ages were proven to be not significant. The study provides a reliable method for estimating household waste generation, providing decision makers useful information for waste management policy development.
KW - Bayesian model average (BMA)
KW - Multivariate linear regression
KW - Municipal solid waste management (MSWM)
KW - Prognosis model
KW - Waste generation
UR - http://www.scopus.com/inward/record.url?scp=85043335333&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85043335333&partnerID=8YFLogxK
U2 - 10.22034/gjesm.2017.03.04.005
DO - 10.22034/gjesm.2017.03.04.005
M3 - Article
AN - SCOPUS:85043335333
VL - 3
SP - 385
EP - 402
JO - Global Journal of Environmental Science and Management
JF - Global Journal of Environmental Science and Management
SN - 2383-3572
IS - 4
ER -