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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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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

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/134687

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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
Author: Solano-Meza, Johanna Orjuela Yepes, David Rodrigo-Ilarri, Javier Cassiraga, Eduardo Fabián
UPV Unit: Universitat Politècnica de València. Departamento de Ingeniería Hidráulica y Medio Ambiente - Departament d'Enginyeria Hidràulica i Medi Ambient
Issued date:
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 ...[+]
Subjects: Environmental science , Waste treatment , Water treatment , Green engineering , Environmental chemical engineering , Waste , Urban solid waste , Artificial intelligence , Urban solid waste management , Tree through machine learning , Support vector machines , Arti&#64257 , Cial neural network
Copyrigths: Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
Source:
Heliyon. (eissn: 2405-8440 )
DOI: 10.1016/j.heliyon.2019.e02810
Publisher:
Elsevier
Publisher version: https://doi.org/10.1016/j.heliyon.2019.e02810
Thanks:
This work was supported by the Santo Tomas University.
Type: Artículo

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