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dc.contributorMalano, Hectoren_US
dc.contributorArora, Meenakshien_US
dc.contributorGeorge, Biju Alummoottilen_US
dc.contributorMaheepala, Shiromaen_US
dc.contributorNawarathna, Bandaraen_US
dc.creatorRathnayaka, Kumuduen_US
dc.date2017-03-01en_US
dc.date.accessioned2017-02-22T23:37:30Z
dc.date.available2017-02-22T23:37:30Z
dc.identifierhttps://www.sciencedirect.com/science/article/pii/S0921344916303305en_US
dc.identifierhttps://mel.cgiar.org/reporting/download/hash/dg5qzztRen_US
dc.identifier.citationKumudu Rathnayaka, Hector Malano, Meenakshi Arora, Biju Alummoottil George, Shiroma Maheepala, Bandara Nawarathna. (1/3/2017). Prediction of urban residential end-use water demands by integrating known and unknown water demand drivers at multiple scales I: Model development. Resources, Conservation and Recycling, 117 (B), pp. 85-92.en_US
dc.identifier.urihttps://hdl.handle.net/20.500.11766/5885
dc.description.abstractDetailed prediction of water demand by their end-uses at multiple scales is essential to support planning of Integrated Urban Water Management, an increasingly applied approach to deal with the problem of water scarcity. This paper presents an urban residential water demand modeling framework that can predict end-use water demand at multiple scales, especially at small scales with a robust explanatory capacity. This is achieved by integrating the complex water demand dynamics of urban residential water use and their underlying variables into a single model. The model described in this study can predict shower, toilet, tap, dishwasher, clothes washer, irrigation, evaporative cooler, bath, and other uses which account for the entire household water use. The model aims to predict water demand at multiple spatial (household/cluster/suburb) and temporal scales (hourly, daily, weekly and seasonal) by considering behavioral differences triggered by factors such as seasonality and presence of people at home. The model incorporates an improved representation of spatial variability by considering behavioral differences between customer groups, and improves the capability to deal with areas with different demographic and housing characteristics. This research confirms the capacity of stochastic modeling methods to represent unexplained behavior of water consumers.en_US
dc.formatPDFen_US
dc.languageenen_US
dc.publisherElsevieren_US
dc.rightsCC-BY-NC-4.0en_US
dc.sourceResources, Conservation and Recycling;117,(2016) Pagination 85,92en_US
dc.subjectspatial variabilityen_US
dc.subjectend-use water demanden_US
dc.subjectresidential water useen_US
dc.subjectstochastic modelingen_US
dc.subjecttemporal variabilityen_US
dc.titlePrediction of urban residential end-use water demands by integrating known and unknown water demand drivers at multiple scales I: Model developmenten_US
dc.typeJournal Articleen_US
cg.creator.idGeorge, Biju Alummoottil: 0000-0002-8427-3350en_US
cg.creator.ID-typeORCIDen_US
cg.contributor.centerThe University of Melbourne, Department of Infrastructure Engineeringen_US
cg.contributor.centerInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.contributor.centerCommonwealth Science and Industrial Research Organisation - CSIROen_US
cg.contributor.centerThe Bureau of Meteorology, Australia Environment and Research Divisionen_US
cg.contributor.crpCGIAR Research Program on Water, Land and Ecosystems - WLEen_US
cg.contributor.funderInternational Water Management Institute - IWMIen_US
cg.contributor.projectCGIAR Research Program on WLE (CRP 5) - WI/W2 Fundingen_US
cg.contributor.project-lead-instituteInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.coverage.regionGlobalen_US
cg.contactk.rathnayaka@student.unimelb.edu.auen_US
cg.identifier.doihttps://dx.doi.org/10.1016/j.resconrec.2016.11.014en_US
dc.identifier.statusOpen accessen_US
mel.project.openhttps://mel.cgiar.org/projects/240en_US
mel.impact-factor3.313en_US


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