Predictive analysis of urban waste generation for the city of Bogot a, Colombia, through the implementation of decision trees-based machine learning, support vector machines and artificial neural networks
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Abstract
This study presents an analysis of three models associated with artificial intelligence as tools to forecast the
generation of urban solid waste in the city of Bogot a, in order to learn about this type of waste's behavior. The
analysis was carried out in such a manner that different efficient alternatives are presented. In this paper, a
possible decision-making strategy was explored and implemented to plan and design technologies for the stages of
collection, transport and final disposal of waste in cities, while taking into account their particular characteristics.
The first model used to analyze data was the decision tree which employed machine learning as a non-parametric
algorithm that models data separation limitations based on the learning decision rules on the input characteristics
of the model. Support vector machines were the second method implemented as a forecasting model. The primary
advantage of support vector machines is their proper adjustment to data despite its variable nature or when faced
with problems with a small amount of training data. Lastly, recurrent neural network models to forecast data were
implemented, which yielded positive results. Their architectural design is useful in exploring temporal correlations
among the same. Distribution by collection zone in the city, socio-economic stratification, population, and
quantity of solid waste generated in a determined period of time were factors considered in the analysis of this
forecast. The results found that support vector machines are the most appropriate model for this type of analysis.
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Atribución-NoComercial-SinDerivadas 2.5 Colombia