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

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 0965-9978

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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 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.

Item Type: Article
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
Subjects: UNSPECIFIED
Departments: Faculty of Science and Technology > Lancaster Environment Centre
ID Code: 52337
Deposited By: ep_importer_pure
Deposited On: 20 Jan 2012 14:30
Refereed?: Yes
Published?: Published
Last Modified: 09 Apr 2014 23:01
Identification Number:
URI: http://eprints.lancs.ac.uk/id/eprint/52337

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