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TitleForecasting household packaging waste generation : a case study
Author(s)Ferreira, João Amaro Oliveira
Figueiredo, Manuel
Oliveira, José A.
Municipal solid waste generation
House packaging waste wollection
Multiple linear regression
Artificial neural networks
House Packaging Waste
Waste Collection
Artificial Neural Network
Issue date2014
PublisherSpringer International Publishing
JournalLecture Notes in Computer Science
Abstract(s)Nowadays, house packaging waste (HPW) materials acquired a great deal of importance, due to environmental and economic reasons, and therefore waste collection companies place thousands of collection points (ecopontos) for people to deposit their HPW. In order to optimize HPW collection process, accurate forecasts of the waste generation rates are needed. Our objective is to develop forecasting models to predict the number of collections per year required for each ecoponto by evaluating the relevance of ten proposed explanatory factors for HPW generation. We developed models based on two approaches: multiple linear regression and artificial neural networks (ANN).The results obtained show that the best ANN model, which achieved an R 2 of 0.672 and MAD of 9.1, slightly outperforms the best regression model (R 2 of 0.636, MAD of 10.44). The most important factors to estimate HPW generation rates are related to ecoponto characteristics and to the population and economic activities around each ecoponto location.
TypeConference paper
AccessOpen access
Appears in Collections:CAlg - Artigos em revistas internacionais/Papers in international journals

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