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

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

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dc.contributor.author Solano-Meza, Johanna es_ES
dc.contributor.author Orjuela Yepes, David es_ES
dc.contributor.author Rodrigo-Ilarri, Javier es_ES
dc.contributor.author Cassiraga, Eduardo Fabián es_ES
dc.date.accessioned 2020-01-16T21:01:50Z
dc.date.available 2020-01-16T21:01:50Z
dc.date.issued 2019 es_ES
dc.identifier.uri http://hdl.handle.net/10251/134687
dc.description.abstract [EN] 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 Bogota, 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. es_ES
dc.description.sponsorship This work was supported by the Santo Tomas University. es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Heliyon es_ES
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Environmental science es_ES
dc.subject Waste treatment es_ES
dc.subject Water treatment es_ES
dc.subject Green engineering es_ES
dc.subject Environmental chemical engineering es_ES
dc.subject Waste es_ES
dc.subject Urban solid waste es_ES
dc.subject Artificial intelligence es_ES
dc.subject Urban solid waste management es_ES
dc.subject Tree through machine learning es_ES
dc.subject Support vector machines es_ES
dc.subject Arti&#64257 es_ES
dc.subject Cial neural network es_ES
dc.subject.classification INGENIERIA HIDRAULICA es_ES
dc.title Predictive analysis of urban waste generation for the city of Bogota , Colombia, through the implementation of decision trees-based machine learning, support vector machines and artifficial neural networks es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.heliyon.2019.e02810 es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Hidráulica y Medio Ambiente - Departament d'Enginyeria Hidràulica i Medi Ambient es_ES
dc.description.bibliographicCitation Solano-Meza, J.; Orjuela Yepes, D.; Rodrigo-Ilarri, J.; Cassiraga, EF. (2019). Predictive analysis of urban waste generation for the city of Bogota , Colombia, through the implementation of decision trees-based machine learning, support vector machines and artifficial neural networks. Heliyon. 5(11). https://doi.org/10.1016/j.heliyon.2019.e02810 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1016/j.heliyon.2019.e02810 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 5 es_ES
dc.description.issue 11 es_ES
dc.identifier.eissn 2405-8440 es_ES
dc.relation.pasarela S\397365 es_ES


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