'Modelling and Simulation Society of Australia and New Zealand'
Abstract
In this-first every study for Chile, a neural network
based hierarchical modelling approach is proposed
for forecasting domestic waste generation for the
whole country. Over 30 global variables from the
342 communes (municipalities) in the country
were analysed extensively using statistical tools
that led to 5 significant explanatory variables:
population, percentage of urban population, years
of education, number of libraries and number of
indigents. The five explanatory variables were
used to develop a feedforward neural network for
predicting volume of global waste generation for a
particular year (2002 in this case) in Chile and
assessing the contribution of variables. The model
had validation R² of 0.82