Predictive analysis of urban waste generation for the city of Bogotá, Colombia, through the implementation of decision trees-based machine learning, support vector machines and artificial neural networks

Johanna Karina Solano Meza; David Orjuela Yepes; Javier Rodrigo-Ilarri; Eduardo Cassiraga

doi:10.1016/j.heliyon.2019.e02810

Predictive analysis of urban waste generation for the city of Bogotá, Colombia, through the implementation of decision trees-based machine learning, support vector machines and artificial neural networks

Heliyon. 2019 Nov 14;5(11):e02810. doi: 10.1016/j.heliyon.2019.e02810. eCollection 2019 Nov.

Authors

Johanna Karina Solano Meza¹, David Orjuela Yepes¹, Javier Rodrigo-Ilarri², Eduardo Cassiraga²

Affiliations

¹ Department of Environmental Engineering, Santo Tomás University, Cra. 9 # 51-11, Bogotá, Colombia.
² Water and Environmental Engineering Institute (IIAMA), Polytechnic University of Valencia, Camino de Vera, s/n, 46022 Valencia, Spain.

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 Bogotá, 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.

Keywords: Artificial intelligence; Artificial neural network; Environmental chemical engineering; Environmental science; Green engineering; Support vector machines; Tree through machine learning; Urban solid waste; Urban solid waste management; Waste; Waste treatment; Water treatment.