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Keywords
(7)
Partial Least Square
Partial Least Square Regression
Regression Model
Statistical Model
Water Distribution System
Water Quality
Relative Error
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Partial least squares regression model to predict water quality in urban water distribution systems
Partial least squares regression model to predict water quality in urban water distribution systems,10.1007/s1220900900252,Transactions of Tianjin
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Partial least squares regression model to predict water quality in urban water distribution systems
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Bijun Luo
,
Yuan Zhao
,
Kai Chen
,
Xinhua Zhao
The
water distribution system
of one residential district in Tianjin is taken as an example to analyze the changes of water quality. Partial least squares (PLS) regression model, in which the turbidity and Fe are regarded as control objectives, is used to establish the statistical model. The experimental results indicate that the PLS
regression model
has good predicted results of
water quality
compared with the monitored data. The percentages of absolute
relative error
(below 15%, 20%, 30%) are 44.4%, 66.7%, 100% (turbidity) and 33.3%, 44.4%, 77.8% (Fe) on the 4th sampling point; 77.8%, 88.9%, 88.9% (turbidity) and 44.4%, 55.6%, 66.7% (Fe) on the 5th sampling point.
Journal:
Transactions of Tianjin University
, vol. 15, no. 2, pp. 140144, 2009
DOI:
10.1007/s1220900900252
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References
(3)
An alternative algorithm to the PLS B problem
(
Citations: 7
)
M. Hanafi
,
E. M. Qannari
Journal:
Computational Statistics & Data Analysis  CS&DA
, vol. 48, no. 1, pp. 6367, 2005
PLS path modeling
(
Citations: 234
)
Michel Tenenhaus
,
Vincenzo Esposito Vinzi
,
Yvesmarie Chatelin
,
Carlo Lauro
Journal:
Computational Statistics & Data Analysis  CS&DA
, vol. 48, no. 1, pp. 159205, 2005
PLS generalised linear regression
(
Citations: 37
)
Philippe Bastien
,
Vincenzo Esposito Vinzi
,
Michel Tenenhaus
Journal:
Computational Statistics & Data Analysis  CS&DA
, vol. 48, no. 1, pp. 1746, 2005