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Procedia Engineering
Article . 2015 . Peer-reviewed
License: CC BY NC ND
Data sources: Crossref
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Procedia Engineering
Article . 2015
License: CC BY NC ND
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Log logistic distribution to model water demand data

Authors: Surendran, Seevali; Tota-Maharaj, Kiran;

Log logistic distribution to model water demand data

Abstract

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.

Keywords

GE, probability distribution function, Water demand, stochastic nature, Minitab, TA, log logistic distribution, Anderson Darling, Engineering(all)

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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
13
Top 10%
Top 10%
Average
Green
gold