Bieroza, M. and Baker, A. and Bridgeman, J. (2012) New data mining and calibration approaches to the assessment of water treatment efficiency. Advances in Engineering Software, 44 (1). pp. 126-135. ISSN 0965-9978Full text not available from this repository.
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.
|Journal or Publication Title:||Advances in Engineering Software|
|Uncontrolled Keywords:||Data mining ; Multivariate analysis ; Pattern recognition ; Artificial neural networks ; Fluorescence spectroscopy ; Organic matter removal ; DISSOLVED ORGANIC-MATTER ; ARTIFICIAL NEURAL-NETWORKS ; FLUORESCENCE SPECTROSCOPY ; BY-PRODUCTS ; CLASSIFICATION ; CARBON ; SPECTRA ; OILS|
|Departments:||Faculty of Science and Technology > Lancaster Environment Centre|
|Deposited On:||20 Jan 2012 14:30|
|Last Modified:||27 Apr 2017 02:46|
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