Skip to main content
Research article - Peer-reviewed, 2012

New data mining and calibration approaches to the assessment of water treatment efficiency

Bieroza, M.; Baker, A.; Bridgeman, J.


For the first time, the application of different robust data mining techniques to the assessment of water treatment performance is considered. Principal components analysis (PCA), parallel factor analysis (PARAFAC), and a self-organizing map (SOM) were used in the analysis of multivariate data characterising organic matter (OM) removal at 16 water treatment works. Decomposed fluorescence data from PCA, PARAFAC and SOM were used as input to calibrate fluorescence data with OM concentrations using stepwise regression (SR), partial least squares (PLS), multiple linear regression (MLR), and neural network with back-propagation algorithm (BPNN). The best results were obtained with combined PARAFAC/PLS and SOM/BPNN. Both the numerical accuracy and feasibility of the adopted solutions were compared and recommendations on the use of the above techniques for fluorescence data analysis are presented.

Published in

Advances in Engineering Software
2012, volume: 44, number: 1, pages: 126 - 135

Authors' information

Bieroza, Magdalena
Lancaster Environment Centre
Baker, Andy
University of New South Wales
Bridgeman, John
University of Birmingham

UKÄ Subject classification

Environmental Sciences

Publication Identifiers


URI (permanent link to this page)