Prediction of urban residential end-use water demands by integrating known and unknown water demand drivers at multiple scales I: Model development

K. Rathnayaka, H. Malano, M. Arora, B. George, S. Maheepala and B. Nawarathna

Resources, Conservation & Recycling, 2017, vol. 117, issue PB, 85-92

Abstract: Detailed 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.

Keywords: End-use water demand; Residential water use; Stochastic modeling; Spatial variability; Temporal variability (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0921344916303305
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:recore:v:117:y:2017:i:pb:p:85-92

DOI: 10.1016/j.resconrec.2016.11.014

Access Statistics for this article

Resources, Conservation & Recycling is currently edited by Ming Xu

More articles in Resources, Conservation & Recycling from Elsevier
Bibliographic data for series maintained by Kai Meng ().