Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - New data mining and calibration approaches to the assessment of water treatment efficiency
AU - Bieroza, M.
AU - Baker, A.
AU - Bridgeman, J.
PY - 2012/2
Y1 - 2012/2
N2 - 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 step-wise 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.
AB - 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 step-wise 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.
KW - Data mining
KW - Multivariate analysis
KW - Pattern recognition
KW - Artificial neural networks
KW - Fluorescence spectroscopy
KW - Organic matter removal
KW - DISSOLVED ORGANIC-MATTER
KW - ARTIFICIAL NEURAL-NETWORKS
KW - FLUORESCENCE SPECTROSCOPY
KW - BY-PRODUCTS
KW - CLASSIFICATION
KW - CARBON
KW - SPECTRA
KW - OILS
U2 - 10.1016/j.advengsoft.2011.05.031
DO - 10.1016/j.advengsoft.2011.05.031
M3 - Journal article
VL - 44
SP - 126
EP - 135
JO - Advances in Engineering Software
JF - Advances in Engineering Software
SN - 0965-9978
IS - 1
ER -