Forecasting water demand in residential, commercial, and industrial zones in Bogotá, Colombia, using least-squares support vector machines

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dc.contributor.authorPeña-Guzmán, Carlos
dc.contributor.authorMelgarejo, Joaquín
dc.contributor.authorPrats, Daniel
dc.date.accessioned2019-12-17T16:08:38Z
dc.date.available2019-12-17T16:08:38Z
dc.date.issued2016
dc.description.abstractThe 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%.spa
dc.description.domainhttp://unidadinvestigacion.usta.edu.cospa
dc.format.mimetypeapplication/pdf
dc.identifier.doihttps://doi.org/10.1155/2016/5712347spa
dc.identifier.urihttp://hdl.handle.net/11634/20406
dc.publisher.branchCRAI-USTA Bogotáspa
dc.relation.referencesG.Ghiassi,D.K. Zimbra, andH. Saidane, “Urbanwater demand forecasting with a dynamic artificial neural network model,” Journal of Water Resources Planning and Management, vol. 134, no. 2, pp. 138–146, 2008.spa
dc.relation.referencesL. A. House-Peters and H. Chang, “Urban water demand modeling: review of concepts, methods, and organizing principles,” Water Resources Research, vol. 47,no. 5, Article IDW05401, 2011.spa
dc.relation.referencesJ. Bougadis, K.Adamowski, and R.Diduch, “Short-termmunicipal water demand forecasting,” Hydrological Processes, vol. 19, no. 1, pp. 137–148, 2005.spa
dc.relation.referencesJ.-D. Rinaudo, “Long-term water demand forecasting,” in Understanding and Managing Urban Water in Transition, Q. Grafton, K. A. Daniell, C. Nauges, J.-D. Rinaudo, andN.W.W. Chan, Eds., vol. 15 of Global Issues inWater Policy, pp. 239–268, Springer, Amsterdam, The Netherlands, 2015.spa
dc.relation.referencesE. A. Donkor, T. A. Mazzuchi, R. Soyer, and A. Roberson, “Urban water demand forecasting: review of methods and models,” Journal ofWater Resources Planning andManagement, vol. 140, no. 2, pp. 146–159, 2014.spa
dc.relation.referencesW. Li and Z. Huicheng, “Urban water demand forecasting based on HP filter and fuzzy neural network,” Journal of Hydroinformatics, vol. 12, no. 2, pp. 172–184, 2010.spa
dc.relation.referencesM. Nasseri, A. Moeini, and M. Tabesh, “Forecasting monthly urbanwater demand using ExtendedKalman Filter andGenetic Programming,” Expert Systems with Applications, vol. 38, no. 6, pp. 7387–7395, 2011.spa
dc.relation.referencesR. B. Billings andC. V. Jones, Forecasting UrbanWater Demand, AmericanWaterWorks Association, 2011.spa
dc.relation.referencesS.Gato,N. Jayasuriya, and P. Roberts, “Temperature and rainfall thresholds for base use urbanwater demandmodelling,” Journal of Hydrology, vol. 337, no. 3-4, pp. 364–376, 2007.spa
dc.relation.referencesM. K. Tiwari and J. Adamowski, “Urban water demand forecasting and uncertainty assessment using ensemble waveletbootstrap- neural network models,” Water Resources Research, vol. 49, no. 10, pp. 6486–6507, 2013.spa
dc.relation.referencesD. Kofinas, N. Mellios, E. Papageorgiou, and C. Laspidou, “Urban water demand forecasting for the Island of Skiathos,” Procedia Engineering, vol. 89, pp. 1023–1030, 2014.spa
dc.relation.referencesR. B. Billings and D. E. Agthe, “State-space versus multiple regression for forecasting urban water demand,” Journal of Water Resources Planning and Management, vol. 124, no. 2, pp. 113–117, 1998.spa
dc.relation.referencesM. S. Babel, A. D. Gupta, and P. Pradhan, “A multivariate econometric approach for domestic water demand modeling: an application to Kathmandu, Nepal,”Water ResourcesManagement, vol. 21, no. 3, pp. 573–589, 2007.spa
dc.relation.referencesA. S. Polebitski and R. Palmer, “Seasonal residential water demand forecasting for census tracts,” Journal of Water Resources Planning and Management, vol. 136, no. 1, pp. 27–36, 2010.spa
dc.relation.referencesA. Altunkaynak,M. ¨ Ozger, andM.C¸akmakci, “Water consumption prediction of Istanbul City by using fuzzy logic approach,” Water Resources Management, vol. 19, no. 5, pp. 641–654, 2005.spa
dc.relation.referencesS. Campisi-Pinto, J. Adamowski, and G. Oron, “Forecasting urban water demand via wavelet-denoising and neural network models. Case study: city of Syracuse, Italy,” Water Resources Management, vol. 26, no. 12, pp. 3539–3558, 2012.spa
dc.relation.referencesM.Herrera, L. Torgo, J. Izquierdo, and R. P´erez-Garc´ıa, “Predictivemodels for forecasting hourly urbanwater demand,” Journal of Hydrology, vol. 387, no. 1-2, pp. 141–150, 2010.spa
dc.relation.referencesJ. Liu, H. H. G. Savenije, and J. Xu, “Forecast of water demand inWeinan City in China using WDF-ANN model,” Physics and Chemistry of the Earth, vol. 28, no. 4-5, pp. 219–224, 2003.spa
dc.relation.referencesJ. S.Wong, Q. Zhang, and Y. D. Chen, “Statistical modeling of daily urban water consumption in Hong Kong: trend, changing patterns, and forecast,”Water Resources Research, vol. 46, no. 3, 2010.spa
dc.relation.referencesL. Chen and T.-Q. Zhang, “Hourly water demand forecast model based on least squares support vector machine,” Journal of Harbin Institute of Technology, vol. 38, no. 9, pp. 1528–1530, 2006.spa
dc.rightsAtribución-NoComercial-CompartirIgual 2.5 Colombia
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/2.5/co/
dc.subject.keywordWater demandspa
dc.subject.keywordLeast-Squares Support Vector Machinesspa
dc.subject.keywordForecastingspa
dc.subject.keywordUrban water cyclespa
dc.titleForecasting water demand in residential, commercial, and industrial zones in Bogotá, Colombia, using least-squares support vector machinesspa
dc.type.categoryGeneración de Nuevo Conocimiento: Artículos publicados en revistas especializadas - Electrónicosspa

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