New data mining and calibration approaches to the assessment of water treatment efficiency
Bieroza, M.; Baker, A.; Bridgeman, J.Abstract
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 Software2012, volume: 44, number: 1, pages: 126 - 135
Authors' information
UKÄ Subject classification
Environmental Sciences
Publication Identifiers
DOI: https://doi.org/10.1016/j.advengsoft.2011.05.031
URI (permanent link to this page)
https://res.slu.se/id/publ/78607