Seevali Surendran
Log logistic distribution to model water demand data
Surendran, Seevali; Tota-Maharaj, Kiran
Authors
Kiran Tota-Maharaj
Abstract
© 2015 The Authors. Published by Elsevier Ltd. There had been insufficient studies previously to conclude the suitability of the appropriate probability distribution functions in modelling water demand. The purpose of this study is to find an appropriate probability density function to apply in simulating water demand using real water consumption data. Daily water consumption data for four years obtained from a water company in UK and analysed using normal, log normal, log logistic and Weibull distributions and a comparison on the applicability of each distribution was assessed. Statistical modelling was performed using MINITAB. The Anderson Darling (AD) statistic was used as the goodness of fit parameter in the analysis.
Presentation Conference Type | Conference Paper (unpublished) |
---|---|
Conference Name | Procedia Engineering |
Publication Date | Jan 1, 2015 |
Deposit Date | Aug 16, 2019 |
Publicly Available Date | Aug 16, 2019 |
Electronic ISSN | 1877-7058 |
Publisher | Elsevier |
Volume | 119 |
Issue | 1 |
Pages | 798-802 |
DOI | https://doi.org/10.1016/j.proeng.2015.08.940 |
Keywords | water demand; stochastic nature; probability distribution function; log logistic distribution; minitab; Anderson Darling |
Public URL | https://uwe-repository.worktribe.com/output/2272703 |
Publisher URL | https://doi.org/10.1016/j.proeng.2015.08.940 |
Files
1-s2.0-S1877705815026107-main
(356 Kb)
PDF
Licence
http://creativecommons.org/licenses/by-nc-nd/4.0/
Publisher Licence URL
http://creativecommons.org/licenses/by-nc-nd/4.0/
Copyright Statement
© 2015 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
Downloadable Citations
About UWE Bristol Research Repository
Administrator e-mail: repository@uwe.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2025
Advanced Search