Forecasting Water Demand in Residential, Commercial, and Industrial Zones in Bogotá, Colombia, Using Least-Squares Support Vector Machines

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Title: Forecasting Water Demand in Residential, Commercial, and Industrial Zones in Bogotá, Colombia, Using Least-Squares Support Vector Machines
Authors: Peña-Guzmán, Carlos | Melgarejo, Joaquín | Prats, Daniel
Research Group/s: Recursos Hídricos y Desarrollo Sostenible
Center, Department or Service: Universidad de Alicante. Departamento de Análisis Económico Aplicado | Universidad de Alicante. Departamento de Ingeniería Química | Universidad de Alicante. Instituto Universitario del Agua y las Ciencias Ambientales
Keywords: Water demand | Forecasting | Least-Squares Support Vector Machines (LS-SVM) | Bogotá
Knowledge Area: Historia e Instituciones Económicas | Ingeniería Química
Issue Date: 2016
Publisher: Hindawi Publishing Corporation
Citation: Mathematical Problems in Engineering. Volume 2016 (2016), Article ID 5712347, 10 pages. doi:10.1155/2016/5712347
Abstract: The Colombian capital, Bogotá, has undergone massive growth in a short period of time. Naturally, this growth has increased the city’s water demand. The prediction of this demand will help understand and analyze consumption behavior, thereby allowing for effective management of the urban water cycle. This paper uses the Least-Squares Support Vector Machines (LS-SVM) model for forecasting residential, industrial, and commercial water demand in the city of Bogotá. The parameters involved in this study include the following: monthly water demand, number of users, and total water consumption bills (price) for the three studied uses. Results provide evidence of the model’s accuracy, producing R2 between 0.8 and 0.98, with an error percentage under 12%.
URI: http://hdl.handle.net/10045/60687
ISSN: 10.1155/2016/5712347 | 1024-123X (Print) | 1563-5147 (Online)
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2016 Carlos Peña-Guzmán et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Peer Review: si
Publisher version: http://dx.doi.org/10.1155/2016/5712347
Appears in Collections:INV - Recursos Hídricos y Desarrollo Sostenible - Artículos de Revistas
INV - Historia e Instituciones Económicas - Artículos de Revistas

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