379
Views
0
CrossRef citations to date
0
Altmetric
Research articles

Development of small district adjustment based on public water demand to decrease the municipal water supply load

&
Pages 142-155 | Received 06 Aug 2013, Accepted 23 Jun 2014, Published online: 15 Sep 2014
 

Abstract

This research microscopically estimates the spatial distribution of water demand and aims to use this to improve the existing zone system. So, this study used geographic information system (GIS) to predict the spatial distribution of water demand according to building unit by applying the basic unit of water use by purpose. Based on the results, the buildings were then grouped into blocks to produce a methodology for controlling small districts using a microscopic approach to decrease the water supply load based on water demand per block. Finally, verification was conducted by quantitatively evaluating the load-decreasing effect through the application of the above methodology. We evaluated efficiency and verified the study's methodology by analysing urban areas that had been Manhattanized and densificated, finding a reduction of approximately 16.7%. The possibility of expanding the study's scope to medium and large districts was suggested.

Acknowledgements

The authors are grateful to the two reviewers for their helpful and constructive comments that helped us to improve the quality and clarity of the paper.

Additional information

Funding

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (NRF-2013R1A2A1A01014020).

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 239.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.