Kanchan Popli|Chunkyoo Park|Sang-Min Han|Seungdo Kim
Prediction of Solid Waste Generation Rates in Urban Region of Laos Using Socio-Demographic and Economic Parameters with a Multi Linear Regression Approach
Hallym University|United Nations|Hallym University|Korea Environment Institute|Hallym University
paper aims develop predictive model laos generate reliable statistics urban solid waste 1995 2050 multilinear regression mlr approach used six different sociodemographic economic parameters ie urban population gross domestic product gdp per capita urban literacy rate urban poverty incidence urban household size urban unemployment rate different reliable models generated four different scenarios value r2 relative measure fit value performance indicators absolute measure fit mean absolute error mae root mean square error rmse mean absolute percentage error mape calculated assure validity accuracy results model 2 scenario 4 estimated best model population gdp per capita show statistical significance estimating urban solid waste generation rate laos amount municipal solid waste estimated 098 million tons mt year 2030 126 mt year 2040 152 mt year 2050 assuming present waste generation trends followed future moreover study provides easy detailed explanation work increase interest researchers allow understand mlr approach clearly inspire use developing countries scarcity data major obstacle field solid waste management drawback study limited availability historical official reliable data statistics laos dependent independent variables