Hybrid Regression Model For Near Real-time Urban Water Demand Forecasting

dc.creatorBrentan
dc.creatorBruno M.; Luvizotto
dc.creatorEdevar
dc.creatorJr.; Herrera
dc.creatorManuel; Izquierdo
dc.creatorJoaquin; Perez-Garcia
dc.creatorRafael
dc.date2017
dc.datejan
dc.date2017-11-13T13:57:20Z
dc.date2017-11-13T13:57:20Z
dc.date.accessioned2018-03-29T06:10:39Z
dc.date.available2018-03-29T06:10:39Z
dc.identifierJournal Of Computational And Applied Mathematics. Elsevier Science Bv, v. 309, p. 532 - 541, 2017.
dc.identifier0377-0427
dc.identifier1879-1778
dc.identifierWOS:000384780100040
dc.identifier10.1016/j.cam.2016.02.009
dc.identifierhttp://www.sciencedirect.com/science/article/pii/S0377042716300565?via%3Dihub
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/329994
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1367019
dc.descriptionThe most important factor in planning and operating water distribution systems is satisfying consumer demand. This means continuously providing users with quality water in adequate volumes at reasonable pressure, thus ensuring reliable water distribution. In recent years, the application of statistical, machine learning, and artificial intelligence methodologies has been fostered for water demand forecasting. However, there is still room for improvement; and new challenges regarding on-line predictive models for water demand have appeared. This work proposes applying support vector regression, as one of the currently better machine learning options for short-term water demand forecasting, to build a base prediction. On this model, a Fourier time series process is built to improve the base prediction. This addition produces a tool able to eliminate many of the errors and much of the bias inherent in a fixed regression structure when responding to new incoming time series data. The final hybrid process is validated using demand data from a water utility in Franca, Brazil. Our model, being a near real-time model for water demand, may be directly exploited in water management decision-making processes. (C) 2016 Elsevier B.V. All rights reserved.
dc.description309
dc.description532
dc.description541
dc.descriptionInternational Conference on Mathematical Modeling in Engineering and Human Behavior
dc.descriptionSEP 09-11, 2015
dc.descriptionInst Univ Matematica Multidisciplinar, Valencia, SPAIN
dc.languageEnglish
dc.publisherElsevier Science BV
dc.publisherAmsterdam
dc.relationJournal of Computational and Applied Mathematics
dc.rightsfechado
dc.sourceWOS
dc.subjectDemand Forecasting
dc.subjectWater Supply
dc.subjectFourier Series
dc.subjectSupport Vector Regression
dc.subjectNear Real-time Algorithms
dc.titleHybrid Regression Model For Near Real-time Urban Water Demand Forecasting
dc.typeActas de congresos


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